Inter-trial Similarity

The motivation for this analysis was to look at the stability of the representations of the stimuli for each trial. Theoretically, while there is only one (or very few) way to do the task correctly, there are many ways to do it incorrectly. The stability of the multivariate representations of each trial might give us insight into how each subject is doing the task, which might have relationships with behavioral or clinical measures.

This analysis was accomplished first by calculating inter-trial similarity: a multivariate pattern was extracted for each trial for each subject from either a bilateral fusiform mask, or a DFR delay load effect mask. Then, trials were broken into correct and incorrect for low and high load trials. From there, each trial was corrrelated to a subject specific “correct trial” template. For incorrect trials, each trial was simply correlated to the mean of all the correct trials. For each correct trial, each trial was correlated to the mean of all correct trials excluding itself.

This resulted in each subject having 64 correlations - one for each trial. We then averaged over trials for four separate conditions - high vs low load, and correct vs incorrect. Thus, each subject ended up with four correlations at each TR, one for each of these conditions, which is what will be used in the present analysis. There should be a grain of caution taken with the low load incorrect trials, because subjects had a low number of trials for this condition.

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(psych)
library(ggplot2)

## 
## Attaching package: 'ggplot2'

## The following objects are masked from 'package:psych':
## 
##     %+%, alpha

library(reshape2)
library(rmatio)
library(patchwork)
library(knitr)
library(kableExtra)

## 
## Attaching package: 'kableExtra'

## The following object is masked from 'package:dplyr':
## 
##     group_rows

load('data/behav.RData')
load('data/DFR_split_groups_info.RData')
load('data/split_groups_info.RData')

# remove outlier from BPRS 
p200_clinical$BPRS_TOT[27] <- NA
p200_data$BPRS_TOT[27] <- NA

source('helper_fxns/split_into_groups.R')

se <- function(x) {
  sd(x,na.rm=TRUE)/sqrt(length(x[!is.na(x)])) 
}

Fusiform

similarity_temp <- read.mat('data/intertrial_similarity_fusiform.mat')

for (i in seq.int(14,17)){
  similarity_temp[[i]] <- data.frame(similarity_temp[[i]])
  similarity_temp[[i]][similarity_temp[[i]]==0] <- NA
  similarity_temp[[i]]$PTID <- constructs_fMRI$PTID
}

cond_avgs <- data.frame(matrix(nrow=4,ncol=14))

cond_avgs[1,] <- colMeans(similarity_temp[[14]][,1:14],na.rm=TRUE)
cond_avgs[2,] <- colMeans(similarity_temp[[15]][,1:14],na.rm=TRUE)
cond_avgs[3,] <- colMeans(similarity_temp[[16]][,1:14],na.rm=TRUE)
cond_avgs[4,] <- colMeans(similarity_temp[[17]][,1:14],na.rm=TRUE)

cond_avgs$group <- factor(names(similarity_temp)[14:17])
colnames(cond_avgs)[1:14] <- c(1:14)

se_avgs <- data.frame(matrix(nrow=4,ncol=14))
se_avgs[1,] <- sapply(similarity_temp[[14]][,1:14],se)
se_avgs[2,] <- sapply(similarity_temp[[15]][,1:14],se)
se_avgs[3,] <- sapply(similarity_temp[[16]][,1:14],se)
se_avgs[4,] <- sapply(similarity_temp[[17]][,1:14],se)
se_avgs$group <- factor(names(similarity_temp)[14:17])
colnames(se_avgs)[1:14] <- c(1:14)

cond_melt <- melt(cond_avgs,id_vars=c("group"))

## Using group as id variables

colnames(cond_melt) <- c("group", "TR", "similarity")
cond_melt$TR <- as.numeric(as.character(cond_melt$TR))

se_melt <- melt(se_avgs,id.vars="group")
colnames(se_melt) <- c("group", "TR", "se")
se_melt$TR <- as.numeric(as.character(se_melt$TR))

melt_avg_data <- merge(cond_melt,se_melt,by=c("group","TR"))
melt_avg_data$se_min <- melt_avg_data$similarity-melt_avg_data$se
melt_avg_data$se_max <- melt_avg_data$similarity+melt_avg_data$se

Average Similarity

First, we just want to plot over time, to see if there is some sort of meaningful increases with our task. There does appear to be - TR 5 is encoding period, TR 8 is peak delay period. Interestingly, there isn’t a peak of similiarity in the probe period.

We also can see a pattern in the split across trial type - we see most similarity in the high load trials. It’s interesting to me that even in the incorrect high load trials, there is higher inter-trial similarity than in the correct low-load trials. But could this be because of the visual differences in the trial types?

It does seem like this measure is useful for distinguishing between both load and accuracy, even when corrected for multiple comparisons. We can distinguish between correct and incorrect trials at high load at TRs 4-6 and 8, at low load for TRs 1, 2, 4-14.

We can distinguish between load for correct trials at TRs 3-7, and for incorrect trials at TRs 1, 2, 4-10, 12 - 14.

ggplot(data=melt_avg_data,aes(x=TR,y=similarity))+
  geom_line(aes(color=group)) +
  geom_ribbon(aes(ymin=se_min,ymax=se_max,fill=group),alpha=0.2)+
  scale_x_continuous(breaks = c(1:14),labels=c(1:14))+
  ggtitle("Intertrial similarity averaged over all subjects")+
  theme_classic()

corrected_p_val <- 0.05/14

low_load_acc_test <- data.frame(matrix(nrow=3,ncol=14))
colnames(low_load_acc_test) <- paste("TR_",c(1:14))
rownames(low_load_acc_test) <- c("t","p","corrected_p")

high_load_acc_test <- data.frame(matrix(nrow=3,ncol=14))
colnames(high_load_acc_test) <- paste("TR_",c(1:14))
rownames(high_load_acc_test) <- c("t","p","corrected_sig")

for (time in seq.int(1,14)){
  low_test <- t.test(similarity_temp[[16]][,time],similarity_temp[[17]][,time],paired=TRUE)
  high_test <- t.test(similarity_temp[[14]][,time],similarity_temp[[15]][,time],paired=TRUE)
  
  low_load_acc_test[1,time] <- low_test$statistic
  low_load_acc_test[2,time] <- low_test$p.value
  if (low_test$p.value < corrected_p_val){low_load_acc_test[3,time] <- 1}else{low_load_acc_test[3,time] <- 0}
  
  high_load_acc_test[1,time] <- high_test$statistic
  high_load_acc_test[2,time] <- high_test$p.value
  if (high_test$p.value < corrected_p_val){high_load_acc_test[3,time] <- 1}else{high_load_acc_test[3,time] <- 0}
  
}

high_load_acc_test %>% 
  kable(format = "html", escape = F) %>%
  kable_styling("striped", full_width = F) %>% 
  add_header_above((c(" ", "t-test between correct and incorrect values at each time point, high load trials" = 14)))

	t-test between correct and incorrect values at each time point, high load trials
	TR_ 1	TR_ 2	TR_ 3	TR_ 4	TR_ 5	TR_ 6	TR_ 7	TR_ 8	TR_ 9	TR_ 10	TR_ 11	TR_ 12	TR_ 13	TR_ 14
t	2.3972061	1.8623274	2.4148361	5.5055990	5.4211117	6.082552	1.693629	3.2985253	2.728558	0.9517419	2.3789578	2.807815	2.8645213	2.2879091
p	0.0176113	0.0642929	0.0168091	0.0000001	0.0000002	0.000000	0.092178	0.0011851	0.007034	0.3425871	0.0184771	0.005575	0.0047064	0.0233819
corrected_sig	0.0000000	0.0000000	0.0000000	1.0000000	1.0000000	1.000000	0.000000	1.0000000	0.000000	0.0000000	0.0000000	0.000000	0.0000000	0.0000000

low_load_acc_test %>% 
  kable(format = "html", escape = F) %>%
  kable_styling("striped", full_width = F) %>% 
  add_header_above((c(" ", "t-test between correct and incorrect values at each time point, low load trials" = 14)))

	t-test between correct and incorrect values at each time point, low load trials
	TR_ 1	TR_ 2	TR_ 3	TR_ 4	TR_ 5	TR_ 6	TR_ 7	TR_ 8	TR_ 9	TR_ 10	TR_ 11	TR_ 12	TR_ 13	TR_ 14
t	5.1068252	3.9361140	2.4773292	4.9781700	8.494834	3.6088515	6.018626	7.974373	7.001935	5.1093126	4.0530884	6.025036	5.6609584	5.0296251
p	0.0000014	0.0001442	0.0147305	0.0000023	0.000000	0.0004612	0.000000	0.000000	0.000000	0.0000013	0.0000936	0.000000	0.0000001	0.0000019
corrected_p	1.0000000	1.0000000	0.0000000	1.0000000	1.000000	1.0000000	1.000000	1.000000	1.000000	1.0000000	1.0000000	1.000000	1.0000000	1.0000000

correct_load_test <- data.frame(matrix(nrow=3,ncol=14))
colnames(correct_load_test) <- paste("TR_",c(1:14))
rownames(correct_load_test) <- c("t","p","corrected_p")

inccorrect_load_test <- data.frame(matrix(nrow=3,ncol=14))
colnames(inccorrect_load_test) <- paste("TR_",c(1:14))
rownames(inccorrect_load_test) <- c("t","p","corrected_p")

for (time in seq.int(1,14)){
  correct_test <- t.test(similarity_temp[[14]][,time],similarity_temp[[16]][,time],paired=TRUE)
  incorrect_test <- t.test(similarity_temp[[15]][,time],similarity_temp[[17]][,time],paired=TRUE)
  
  correct_load_test[1,time] <- correct_test$statistic
  correct_load_test[2,time] <- correct_test$p.value
  if (correct_test$p.value < corrected_p_val){correct_load_test[3,time] <- 1}else{correct_load_test[3,time] <- 0}
  
  inccorrect_load_test[1,time] <- incorrect_test$statistic
  inccorrect_load_test[2,time] <- incorrect_test$p.value
  if (incorrect_test$p.value < corrected_p_val){inccorrect_load_test[3,time] <- 1}else{inccorrect_load_test[3,time] <- 0}
  
}

correct_load_test %>% 
  kable(format = "html", escape = F) %>%
  kable_styling("striped", full_width = F) %>% 
  add_header_above((c(" ", "t-test between high and low loads at each time point, correct trials" = 14)))

	t-test between high and low loads at each time point, correct trials
	TR_ 1	TR_ 2	TR_ 3	TR_ 4	TR_ 5	TR_ 6	TR_ 7	TR_ 8	TR_ 9	TR_ 10	TR_ 11	TR_ 12	TR_ 13	TR_ 14
t	1.7949581	0.9966598	3.3287628	11.21938	15.70624	15.78625	3.0118226	-0.4521697	0.0242294	-0.7152968	0.2766772	1.0200597	1.1948411	1.094814
p	0.0744474	0.3203545	0.0010708	0.00000	0.00000	0.00000	0.0029958	0.6517266	0.9806982	0.4754127	0.7823660	0.3091582	0.2338233	0.275156
corrected_p	0.0000000	0.0000000	1.0000000	1.00000	1.00000	1.00000	1.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.000000

inccorrect_load_test %>% 
  kable(format = "html", escape = F) %>%
  kable_styling("striped", full_width = F) %>% 
  add_header_above((c(" ", "t-test between high and low loads at each time point, incorrect trials" = 14)))

	t-test between high and low loads at each time point, incorrect trials
	TR_ 1	TR_ 2	TR_ 3	TR_ 4	TR_ 5	TR_ 6	TR_ 7	TR_ 8	TR_ 9	TR_ 10	TR_ 11	TR_ 12	TR_ 13	TR_ 14
t	4.7608971	3.7080176	2.696950	5.5892848	11.71409	7.428207	4.9743285	5.6745823	5.7259048	3.8951079	2.5802842	5.174988	4.5173524	4.3885746
p	0.0000058	0.0003266	0.008079	0.0000002	0.00000	0.000000	0.0000024	0.0000001	0.0000001	0.0001674	0.0111653	0.000001	0.0000156	0.0000259
corrected_p	1.0000000	1.0000000	0.000000	1.0000000	1.00000	1.000000	1.0000000	1.0000000	1.0000000	1.0000000	0.0000000	1.000000	1.0000000	1.0000000

split_similarity <- list()
split_sim_avgs <- list()


for (i in seq.int(14,17)){ 
  split_similarity[[names(similarity_temp)[i]]] <- split_into_groups(similarity_temp[[i]],WM_groups)
  colnames(split_similarity[[i-13]][["all"]])[1:14] <- c(1:14)
  
  for (level in seq.int(1,3)){
    temp_data <- data.frame(mean=colMeans(split_similarity[[i-13]][[level]][1:14],na.rm=TRUE),se = sapply(split_similarity[[i-13]][[level]][1:14],se),
                            se_min = colMeans(split_similarity[[i-13]][[level]][1:14],na.rm=TRUE) - sapply(split_similarity[[i-13]][[level]][1:14],se),
                            se_max = colMeans(split_similarity[[i-13]][[level]][1:14],na.rm=TRUE) + sapply(split_similarity[[i-13]][[level]][1:14],se))
    split_sim_avgs[[names(split_similarity)[i-13]]][[names(split_similarity[[i-13]])[level]]] <- data.frame((temp_data))
    split_sim_avgs[[i-13]][[level]]$group <- rep(names(split_similarity[[i-13]])[level],14)
    split_sim_avgs[[i-13]][[level]]$TR <- seq.int(1,14)
    
  }
  
  split_sim_avgs[[i-13]][["all"]] <- rbind(split_sim_avgs[[i-13]][["high"]],split_sim_avgs[[i-13]][["med"]],split_sim_avgs[[i-13]][["low"]])
  
  split_sim_avgs[[i-13]][["all"]]$group <- factor(split_sim_avgs[[i-13]][["all"]]$group, levels=c("high","med","low"))
  
}

Split into groups

The only thing that pops out here for me is that during the delay period (around TR 8), the high capacity group shows more similarity than the medium or low capacity groups, particularly for the high load trials. Also interesting to note that for the high load incorrect trials, we still see peaks that make sense with our task, while for the low load incorrect trial, there isn’t really any informative shape to the curves.

sim_plots <- list()

for (i in seq.int(1,4)){
  sim_plots[[i]] <- ggplot(data = split_sim_avgs[[i]][["all"]])+
    geom_line(aes(x=TR,y=mean,color=group))+
    geom_ribbon(aes(x=TR,ymin=se_min,ymax=se_max,fill=group),alpha=0.2)+
    scale_x_continuous(breaks = c(1:14),labels=c(1:14))+
    ggtitle(names(split_sim_avgs)[i])+
    ylab("Mean Similarity")+
    theme_classic()
  
}

(sim_plots[[1]] + sim_plots[[2]]) / (sim_plots[[3]] + sim_plots[[4]])+
  plot_layout(guides = "collect")+
  plot_annotation(title="Inter-trial Similarity")

data_to_plot <- merge(constructs_fMRI,p200_data,by="PTID")
data_to_plot <- merge(data_to_plot,p200_clinical_zscores,by="PTID")

data_to_plot <- data_to_plot[,c(1,6,7,13,14,40,41)]
data_to_plot$ACC_LE <- data_to_plot$XDFR_MRI_ACC_L3 - data_to_plot$XDFR_MRI_ACC_L1

corr_to_behav_plots <- list()

for (i in seq.int(14,17)){
  measure_by_time <- data.frame(matrix(nrow=4,ncol=14))
  
  for (measure in seq.int(3,6)){
    for (TR in seq.int(1,14)){
      measure_by_time[measure-2,TR] <- cor(data_to_plot[,measure],similarity_temp[[i]][,TR],use = "pairwise.complete.obs")
    }
  }
  
  measure_by_time <- data.frame(t(measure_by_time))
  measure_by_time$TR <- seq.int(1,14)
  
  colnames(measure_by_time)[1:4] <- colnames(data_to_plot)[3:6]
  
  melted_measure_by_time <- melt(measure_by_time,id.vars="TR")
  
  corr_to_behav_plots[[names(similarity_temp)[i]]] <- ggplot(data=melted_measure_by_time,aes(x=TR,y=value))+
    geom_line(aes(color=variable))+
    scale_x_continuous(breaks = c(1:14),labels=c(1:14))+
    ggtitle(names(similarity_temp)[i])+
    theme_classic()
  
}

Compare to behavioral measures

Over time

If we just look at the correlation over time for a number of behavioral and clinical measures, the most obvious thing to note is that the similarity, overall, had the highest correlation with accuracy, especially in encoding. Also note that the correlation with omnibus span peaks in the delay period, though it is overall a weaker correlation. Similarly, see a relatively weak negative correlation with BPRS total in the encoding and delay periods. Also interestingly, accuracy at low load is negatively correlated with similarity in the low load inccorect trials - that is, the stronger the correlations between a incorrect trials and the correct template in an individual, the more poorly they do overall. This finding should be interpreted with caution though, because there are few incorrect low load trials overall.

(corr_to_behav_plots[[1]] + corr_to_behav_plots[[2]]) / (corr_to_behav_plots[[3]] + corr_to_behav_plots[[4]])+
  plot_layout(guides="collect")+
  plot_annotation(title = "Correlation between inter-trial similarity and behavioral measure")

scatter_plots_delay <- list()
scatter_plots_cue <- list()
scatter_plots_probe <- list()

for (i in seq.int(14,17)){
  temp_plot_data <- merge(data_to_plot,similarity_temp[[i]],by="PTID")
  scatter_plots_delay[[names(similarity_temp)[i]]][["omnibus"]]  <- ggplot(data=temp_plot_data)+
    geom_point(aes(x=omnibus_span_no_DFR_MRI,y=X8))+
    stat_smooth(aes(x=omnibus_span_no_DFR_MRI,y=X8),method="lm")+
    scale_x_continuous(breaks = c(1:14),labels=c(1:14))+
    ggtitle(names(similarity_temp)[i])+
    ylab("Inter-trial similarity")+
    theme_classic()
  
  scatter_plots_delay[[names(similarity_temp)[i]]][["BPRS"]]  <- ggplot(data=temp_plot_data)+
    geom_point(aes(x=BPRS_TOT.x,y=X8))+
    stat_smooth(aes(x=BPRS_TOT.x,y=X8),method="lm")+
    scale_x_continuous(breaks = c(1:14),labels=c(1:14))+
    ggtitle(names(similarity_temp)[i])+
    ylab("Inter-trial similarity")+
    theme_classic()
  
  scatter_plots_delay[[names(similarity_temp)[i]]][["L3_acc"]]  <- ggplot(data=temp_plot_data)+
    geom_point(aes(x=XDFR_MRI_ACC_L3,y=X8))+
    stat_smooth(aes(x=XDFR_MRI_ACC_L3,y=X8),method="lm")+
    scale_x_continuous(breaks = c(1:14),labels=c(1:14))+
    ggtitle(names(similarity_temp)[i])+
    ylab("Inter-trial similarity")+
    theme_classic()
  
  scatter_plots_cue[[names(similarity_temp)[i]]][["omnibus"]]  <- ggplot(data=temp_plot_data)+
    geom_point(aes(x=omnibus_span_no_DFR_MRI,y=X6))+
    stat_smooth(aes(x=omnibus_span_no_DFR_MRI,y=X6),method="lm")+
    scale_x_continuous(breaks = c(1:14),labels=c(1:14))+
    ggtitle(names(similarity_temp)[i])+
    ylab("Inter-trial similarity")+
    theme_classic()
  
  scatter_plots_cue[[names(similarity_temp)[i]]][["BPRS"]]  <- ggplot(data=temp_plot_data)+
    geom_point(aes(x=BPRS_TOT.x,y=X6))+
    stat_smooth(aes(x=BPRS_TOT.x,y=X6),method="lm")+
    scale_x_continuous(breaks = c(1:14),labels=c(1:14))+
    ggtitle(names(similarity_temp)[i])+
    ylab("Inter-trial similarity")+
    theme_classic()
  
  scatter_plots_cue[[names(similarity_temp)[i]]][["L3_acc"]]  <- ggplot(data=temp_plot_data)+
    geom_point(aes(x=XDFR_MRI_ACC_L3,y=X6))+
    stat_smooth(aes(x=XDFR_MRI_ACC_L3,y=X6),method="lm")+
    scale_x_continuous(breaks = c(1:14),labels=c(1:14))+
    ggtitle(names(similarity_temp)[i])+
    ylab("Inter-trial similarity")+
    theme_classic()
  
    
    scatter_plots_probe[[names(similarity_temp)[i]]][["omnibus"]]  <- ggplot(data=temp_plot_data)+
    geom_point(aes(x=omnibus_span_no_DFR_MRI,y=X11))+
    stat_smooth(aes(x=omnibus_span_no_DFR_MRI,y=X11),method="lm")+
    scale_x_continuous(breaks = c(1:14),labels=c(1:14))+
    ggtitle(names(similarity_temp)[i])+
    ylab("Inter-trial similarity")+
    theme_classic()
  
  scatter_plots_probe[[names(similarity_temp)[i]]][["BPRS"]]  <- ggplot(data=temp_plot_data)+
    geom_point(aes(x=BPRS_TOT.x,y=X11))+
    stat_smooth(aes(x=BPRS_TOT.x,y=X11),method="lm")+
    scale_x_continuous(breaks = c(1:14),labels=c(1:14))+
    ggtitle(names(similarity_temp)[i])+
    ylab("Inter-trial similarity")+
    theme_classic()
  
  scatter_plots_probe[[names(similarity_temp)[i]]][["L3_acc"]]  <- ggplot(data=temp_plot_data)+
    geom_point(aes(x=XDFR_MRI_ACC_L3,y=X11))+
    stat_smooth(aes(x=XDFR_MRI_ACC_L3,y=X11),method="lm")+
    scale_x_continuous(breaks = c(1:14),labels=c(1:14))+
    ggtitle(names(similarity_temp)[i])+
    ylab("Inter-trial similarity")+
    theme_classic()
}

Encoding Period

The main point of these analyses were to make sure that we weren’t missing any non-linear relationships that would be obscured in a linear correlation. We don’t really see any of these. The only significant linear correlations are those with accuracy.

(scatter_plots_cue[[1]][["omnibus"]] + scatter_plots_cue[[2]][["omnibus"]]) /
  (scatter_plots_cue[[3]][["omnibus"]] + scatter_plots_cue[[4]][["omnibus"]])+
  plot_layout(guides="collect")+
  plot_annotation(title = "Omnibus span vs inter-trial similiarity - encoding")

## Warning: Removed 57 rows containing non-finite values (stat_smooth).

## Warning: Removed 57 rows containing missing values (geom_point).

cor.test(similarity_temp[["high_correct_avg"]]$X5,data_to_plot$omnibus_span_no_DFR_MRI)

## 
##  Pearson's product-moment correlation
## 
## data:  similarity_temp[["high_correct_avg"]]$X5 and data_to_plot$omnibus_span_no_DFR_MRI
## t = 1.0422, df = 168, p-value = 0.2988
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.07122842  0.22791004
## sample estimates:
##        cor 
## 0.08014506

cor.test(similarity_temp[["high_incorrect_avg"]]$X5,data_to_plot$omnibus_span_no_DFR_MRI)

## 
##  Pearson's product-moment correlation
## 
## data:  similarity_temp[["high_incorrect_avg"]]$X5 and data_to_plot$omnibus_span_no_DFR_MRI
## t = 1.3057, df = 168, p-value = 0.1935
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.05105816  0.24701425
## sample estimates:
##       cor 
## 0.1002263

(scatter_plots_cue[[1]][["BPRS"]] + scatter_plots_cue[[2]][["BPRS"]]) /
  (scatter_plots_cue[[3]][["BPRS"]] + scatter_plots_cue[[4]][["BPRS"]])+
  plot_layout(guides="collect")+
  plot_annotation(title = "BPRS span vs inter-trial similiarity - encoding")

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 58 rows containing non-finite values (stat_smooth).

## Warning: Removed 58 rows containing missing values (geom_point).

cor.test(similarity_temp[["high_correct_avg"]]$X5,data_to_plot$BPRS_TOT.x)

## 
##  Pearson's product-moment correlation
## 
## data:  similarity_temp[["high_correct_avg"]]$X5 and data_to_plot$BPRS_TOT.x
## t = -1.2081, df = 167, p-value = 0.2287
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.24065781  0.05870559
## sample estimates:
##         cor 
## -0.09307933

cor.test(similarity_temp[["high_incorrect_avg"]]$X5,data_to_plot$BPRS_TOT.x)

## 
##  Pearson's product-moment correlation
## 
## data:  similarity_temp[["high_incorrect_avg"]]$X5 and data_to_plot$BPRS_TOT.x
## t = -0.56576, df = 167, p-value = 0.5723
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.1934207  0.1079349
## sample estimates:
##         cor 
## -0.04373779

(scatter_plots_cue[[1]][["L3_acc"]] + scatter_plots_cue[[2]][["L3_acc"]]) /
  (scatter_plots_cue[[3]][["L3_acc"]] + scatter_plots_cue[[4]][["L3_acc"]])+
  plot_layout(guides="collect")+
  plot_annotation(title = "L3_acc span vs inter-trial similiarity - encoding")

## Warning: Removed 57 rows containing non-finite values (stat_smooth).

## Warning: Removed 57 rows containing missing values (geom_point).

cor.test(similarity_temp[["high_correct_avg"]]$X5,data_to_plot$XDFR_MRI_ACC_L3)

## 
##  Pearson's product-moment correlation
## 
## data:  similarity_temp[["high_correct_avg"]]$X5 and data_to_plot$XDFR_MRI_ACC_L3
## t = 3.8309, df = 168, p-value = 0.0001801
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1388500 0.4161991
## sample estimates:
##       cor 
## 0.2834407

cor.test(similarity_temp[["high_incorrect_avg"]]$X5,data_to_plot$XDFR_MRI_ACC_L3)

## 
##  Pearson's product-moment correlation
## 
## data:  similarity_temp[["high_incorrect_avg"]]$X5 and data_to_plot$XDFR_MRI_ACC_L3
## t = 3.3638, df = 168, p-value = 0.0009523
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1046443 0.3870809
## sample estimates:
##      cor 
## 0.251202

cor.test(similarity_temp[["low_correct_avg"]]$X5,data_to_plot$XDFR_MRI_ACC_L3)

## 
##  Pearson's product-moment correlation
## 
## data:  similarity_temp[["low_correct_avg"]]$X5 and data_to_plot$XDFR_MRI_ACC_L3
## t = 2.8135, df = 168, p-value = 0.005486
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.06364264 0.35141913
## sample estimates:
##       cor 
## 0.2121249

cor.test(similarity_temp[["low_incorrect_avg"]]$X5,data_to_plot$XDFR_MRI_ACC_L3)

## 
##  Pearson's product-moment correlation
## 
## data:  similarity_temp[["low_incorrect_avg"]]$X5 and data_to_plot$XDFR_MRI_ACC_L3
## t = 0.23759, df = 111, p-value = 0.8126
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.1628625  0.2064158
## sample estimates:
##        cor 
## 0.02254565

Delay Period

No non-linear here, either. The linear relationships with accuracy all are statistically significant, though the relationship with omnibus span only trends towards significance.

(scatter_plots_delay[[1]][["omnibus"]] + scatter_plots_delay[[2]][["omnibus"]]) /
  (scatter_plots_delay[[3]][["omnibus"]] + scatter_plots_delay[[4]][["omnibus"]])+
  plot_layout(guides="collect")+
  plot_annotation(title = "Omnibus span vs inter-trial similiarity - delay")

## Warning: Removed 57 rows containing non-finite values (stat_smooth).

## Warning: Removed 57 rows containing missing values (geom_point).

cor.test(similarity_temp[["high_correct_avg"]]$X8,data_to_plot$omnibus_span_no_DFR_MRI)

## 
##  Pearson's product-moment correlation
## 
## data:  similarity_temp[["high_correct_avg"]]$X8 and data_to_plot$omnibus_span_no_DFR_MRI
## t = 1.8897, df = 168, p-value = 0.06053
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.006387842  0.288514575
## sample estimates:
##       cor 
## 0.1442651

(scatter_plots_delay[[1]][["BPRS"]] + scatter_plots_delay[[2]][["BPRS"]]) /
  (scatter_plots_delay[[3]][["BPRS"]] + scatter_plots_delay[[4]][["BPRS"]])+
  plot_layout(guides="collect")+
  plot_annotation(title = "BPRS vs inter-trial similiarity - delay")

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 58 rows containing non-finite values (stat_smooth).

## Warning: Removed 58 rows containing missing values (geom_point).

cor.test(similarity_temp[["high_correct_avg"]]$X8,data_to_plot$BPRS_TOT.x)

## 
##  Pearson's product-moment correlation
## 
## data:  similarity_temp[["high_correct_avg"]]$X8 and data_to_plot$BPRS_TOT.x
## t = -1.5374, df = 167, p-value = 0.1261
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.26438049  0.03342069
## sample estimates:
##        cor 
## -0.1181354

cor.test(similarity_temp[["high_incorrect_avg"]]$X8,data_to_plot$BPRS_TOT.x)

## 
##  Pearson's product-moment correlation
## 
## data:  similarity_temp[["high_incorrect_avg"]]$X8 and data_to_plot$BPRS_TOT.x
## t = 0.10242, df = 167, p-value = 0.9185
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.1432065  0.1586951
## sample estimates:
##         cor 
## 0.007924895

(scatter_plots_delay[[1]][["L3_acc"]] + scatter_plots_delay[[2]][["L3_acc"]]) /
  (scatter_plots_delay[[3]][["L3_acc"]] + scatter_plots_delay[[4]][["L3_acc"]])+
  plot_layout(guides="collect")+
  plot_annotation(title = "L3_acc vs inter-trial similiarity - delay")

## Warning: Removed 57 rows containing non-finite values (stat_smooth).

## Warning: Removed 57 rows containing missing values (geom_point).

cor.test(similarity_temp[["high_correct_avg"]]$X8,data_to_plot$XDFR_MRI_ACC_L3)

## 
##  Pearson's product-moment correlation
## 
## data:  similarity_temp[["high_correct_avg"]]$X8 and data_to_plot$XDFR_MRI_ACC_L3
## t = 2.5743, df = 168, p-value = 0.01091
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.0456270 0.3354812
## sample estimates:
##       cor 
## 0.1948034

cor.test(similarity_temp[["high_incorrect_avg"]]$X8,data_to_plot$XDFR_MRI_ACC_L3)

## 
##  Pearson's product-moment correlation
## 
## data:  similarity_temp[["high_incorrect_avg"]]$X8 and data_to_plot$XDFR_MRI_ACC_L3
## t = 2.5362, df = 168, p-value = 0.01212
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.04275452 0.33292450
## sample estimates:
##      cor 
## 0.192033

cor.test(similarity_temp[["low_correct_avg"]]$X8,data_to_plot$XDFR_MRI_ACC_L3)

## 
##  Pearson's product-moment correlation
## 
## data:  similarity_temp[["low_correct_avg"]]$X8 and data_to_plot$XDFR_MRI_ACC_L3
## t = 2.2986, df = 168, p-value = 0.02276
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.02475484 0.31680625
## sample estimates:
##       cor 
## 0.1746185

cor.test(similarity_temp[["low_incorrect_avg"]]$X8,data_to_plot$XDFR_MRI_ACC_L3)

## 
##  Pearson's product-moment correlation
## 
## data:  similarity_temp[["low_incorrect_avg"]]$X8 and data_to_plot$XDFR_MRI_ACC_L3
## t = -0.91105, df = 111, p-value = 0.3642
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.2666375  0.1001729
## sample estimates:
##         cor 
## -0.08615111

Probe Period

Here, we’re also seeing some correlations with accuracy for correct trials (irrespective of load) and with BPRS at low load correct trials.

(scatter_plots_probe[[1]][["omnibus"]] + scatter_plots_probe[[2]][["omnibus"]]) /
  (scatter_plots_probe[[3]][["omnibus"]] + scatter_plots_probe[[4]][["omnibus"]])+
  plot_layout(guides="collect")+
  plot_annotation(title = "Omnibus span vs inter-trial similiarity - probe")

## Warning: Removed 57 rows containing non-finite values (stat_smooth).

## Warning: Removed 57 rows containing missing values (geom_point).

cor.test(similarity_temp[["high_correct_avg"]]$X11,data_to_plot$omnibus_span_no_DFR_MRI)

## 
##  Pearson's product-moment correlation
## 
## data:  similarity_temp[["high_correct_avg"]]$X11 and data_to_plot$omnibus_span_no_DFR_MRI
## t = 1.2632, df = 168, p-value = 0.2083
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.05431093  0.24394899
## sample estimates:
##        cor 
## 0.09699623

(scatter_plots_probe[[1]][["BPRS"]] + scatter_plots_probe[[2]][["BPRS"]]) /
  (scatter_plots_probe[[3]][["BPRS"]] + scatter_plots_probe[[4]][["BPRS"]])+
  plot_layout(guides="collect")+
  plot_annotation(title = "BPRS vs inter-trial similiarity - probe")

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 58 rows containing non-finite values (stat_smooth).

## Warning: Removed 58 rows containing missing values (geom_point).

cor.test(similarity_temp[["low_correct_avg"]]$X11,data_to_plot$BPRS_TOT.x)

## 
##  Pearson's product-moment correlation
## 
## data:  similarity_temp[["low_correct_avg"]]$X11 and data_to_plot$BPRS_TOT.x
## t = 2.2522, df = 167, p-value = 0.02561
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.02128244 0.31449955
## sample estimates:
##       cor 
## 0.1716909

(scatter_plots_probe[[1]][["L3_acc"]] + scatter_plots_probe[[2]][["L3_acc"]]) /
  (scatter_plots_probe[[3]][["L3_acc"]] + scatter_plots_probe[[4]][["L3_acc"]])+
  plot_layout(guides="collect")+
  plot_annotation(title = "L3_acc vs inter-trial similiarity - probe")

## Warning: Removed 57 rows containing non-finite values (stat_smooth).

## Warning: Removed 57 rows containing missing values (geom_point).

cor.test(similarity_temp[["high_correct_avg"]]$X11,data_to_plot$XDFR_MRI_ACC_L3)

## 
##  Pearson's product-moment correlation
## 
## data:  similarity_temp[["high_correct_avg"]]$X11 and data_to_plot$XDFR_MRI_ACC_L3
## t = 3.6306, df = 168, p-value = 0.0003751
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1242588 0.4038472
## sample estimates:
##       cor 
## 0.2697284

cor.test(similarity_temp[["high_incorrect_avg"]]$X11,data_to_plot$XDFR_MRI_ACC_L3)

## 
##  Pearson's product-moment correlation
## 
## data:  similarity_temp[["high_incorrect_avg"]]$X11 and data_to_plot$XDFR_MRI_ACC_L3
## t = 1.5842, df = 168, p-value = 0.115
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.02973581  0.26696082
## sample estimates:
##       cor 
## 0.1213214

cor.test(similarity_temp[["low_correct_avg"]]$X11,data_to_plot$XDFR_MRI_ACC_L3)

## 
##  Pearson's product-moment correlation
## 
## data:  similarity_temp[["low_correct_avg"]]$X11 and data_to_plot$XDFR_MRI_ACC_L3
## t = 2.6079, df = 168, p-value = 0.009931
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.0481640 0.3377357
## sample estimates:
##       cor 
## 0.1972483

cor.test(similarity_temp[["low_incorrect_avg"]]$X11,data_to_plot$XDFR_MRI_ACC_L3)

## 
##  Pearson's product-moment correlation
## 
## data:  similarity_temp[["low_incorrect_avg"]]$X11 and data_to_plot$XDFR_MRI_ACC_L3
## t = 0.43986, df = 111, p-value = 0.6609
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.1441271  0.2247116
## sample estimates:
##        cor 
## 0.04171328

Comparing encoding to delay

The analysis we were looking at before only looked within a single TR - now, let’s look at the fidelity across TRs representing encoding and delay, and how that relates to different behavioral measures. We’ll look at 3 different things - first, just comparison across any given trial. Then, we’ll look at encoding vs correct average delay, and delay vs correct average encoding.

encoding_to_delay_plots <- list()

for (i in c(6,10,12)){
  colnames(similarity_temp[[i]]) <- unlist(similarity_temp[[1]][[1]])
  similarity_temp[[i]][similarity_temp[[i]]==0] <- NA
  temp_plot_data <- cbind.data.frame(data_to_plot,similarity_temp[[i]])
  
  encoding_to_delay_plots[[names(similarity_temp)[i]]][["omnibus"]][["low_load_incorrect"]] <- ggplot(data = temp_plot_data,aes(x=omnibus_span_no_DFR_MRI,y=`low load incorrect`))+
    geom_point()+
    stat_smooth(method="lm")+
    ylab("Similarity")+
    ggtitle("Low Load - Incorrect trials")+
    theme_classic()
  
  encoding_to_delay_plots[[names(similarity_temp)[i]]][["omnibus"]][["high_load_incorrect"]] <- ggplot(data = temp_plot_data,aes(x=omnibus_span_no_DFR_MRI,y=`high load incorrect`))+
    geom_point()+
    stat_smooth(method="lm")+
    ylab("Similarity")+
    ggtitle("High Load - Incorrect trials")+
    theme_classic()
  
  encoding_to_delay_plots[[names(similarity_temp)[i]]][["omnibus"]][["low_load_correct"]] <- ggplot(data = temp_plot_data,aes(x=omnibus_span_no_DFR_MRI,y=`low load correct`))+
    geom_point()+
    stat_smooth(method="lm")+
    ylab("Similarity")+
    ggtitle("Low Load - Correct trials")+
    theme_classic()
  
  encoding_to_delay_plots[[names(similarity_temp)[i]]][["omnibus"]][["high_load_correct"]] <- ggplot(data = temp_plot_data,aes(x=omnibus_span_no_DFR_MRI,y=`high load correct`))+
    geom_point()+
    stat_smooth(method="lm")+
    ylab("Similarity")+
    ggtitle("High Load - Correct trials")+
    theme_classic()
  
  encoding_to_delay_plots[[names(similarity_temp)[i]]][["L3_Acc"]][["low_load_incorrect"]] <- ggplot(data = temp_plot_data,aes(x=XDFR_MRI_ACC_L3,y=`low load incorrect`))+
    geom_point()+
    stat_smooth(method="lm")+
    ylab("Similarity")+
    ggtitle("Low Load - Incorrect trials")+
    theme_classic()
  
  encoding_to_delay_plots[[names(similarity_temp)[i]]][["L3_Acc"]][["high_load_incorrect"]] <- ggplot(data = temp_plot_data,aes(x=XDFR_MRI_ACC_L3,y=`high load incorrect`))+
    geom_point()+
    stat_smooth(method="lm")+
    ylab("Similarity")+
    ggtitle("High Load - Incorrect trials")+
    theme_classic()
  
  encoding_to_delay_plots[[names(similarity_temp)[i]]][["L3_Acc"]][["low_load_correct"]] <- ggplot(data = temp_plot_data,aes(x=XDFR_MRI_ACC_L3,y=`low load correct`))+
    geom_point()+
    stat_smooth(method="lm")+
    ylab("Similarity")+
    ggtitle("Low Load - Correct trials")+
    theme_classic()
  
  encoding_to_delay_plots[[names(similarity_temp)[i]]][["L3_Acc"]][["high_load_correct"]] <- ggplot(data = temp_plot_data,aes(x=XDFR_MRI_ACC_L3,y=`high load correct`))+
    geom_point()+
    stat_smooth(method="lm")+
    ylab("Similarity")+
    ggtitle("High Load - Correct trials")+
    theme_classic()
  
  encoding_to_delay_plots[[names(similarity_temp)[i]]][["BPRS"]][["low_load_incorrect"]] <- ggplot(data = temp_plot_data,aes(x=BPRS_TOT.x,y=`low load incorrect`))+
    geom_point()+
    stat_smooth(method="lm")+
    ylab("Similarity")+
    ggtitle("Low Load - Incorrect trials")+
    theme_classic()
  
  encoding_to_delay_plots[[names(similarity_temp)[i]]][["BPRS"]][["high_load_incorrect"]] <- ggplot(data = temp_plot_data,aes(x=BPRS_TOT.x,y=`high load incorrect`))+
    geom_point()+
    stat_smooth(method="lm")+
    ylab("Similarity")+
    ggtitle("High Load - Incorrect trials")+
    theme_classic()
  
  encoding_to_delay_plots[[names(similarity_temp)[i]]][["BPRS"]][["low_load_correct"]] <- ggplot(data = temp_plot_data,aes(x=BPRS_TOT.x,y=`low load correct`))+
    geom_point()+
    stat_smooth(method="lm")+
    ylab("Similarity")+
    ggtitle("Low Load - Correct trials")+
    theme_classic()
  
  encoding_to_delay_plots[[names(similarity_temp)[i]]][["BPRS"]][["high_load_correct"]] <- ggplot(data = temp_plot_data,aes(x=BPRS_TOT.x,y=`high load correct`))+
    geom_point()+
    stat_smooth(method="lm")+
    ylab("Similarity")+
    ggtitle("High Load - Correct trials")+
    theme_classic()
}

temp_plot_data <- cbind.data.frame(data_to_plot,similarity_temp[["correct_encoding_to_correct_delay"]])
colnames(temp_plot_data)[9] <- "correct_encoding_delay"

encoding_to_delay_plots[["correct_encoding_to_correct_delay"]][["omnibus"]] <- ggplot(data = 
                                                                                        temp_plot_data,aes(x=omnibus_span_no_DFR_MRI,y=correct_encoding_delay))+
  geom_point()+
  stat_smooth(method="lm")+
  ylab("Similarity")+
  ggtitle("Template encoding/delay vs Omnibus Span")+
  theme_classic()

encoding_to_delay_plots[["correct_encoding_to_correct_delay"]][["BPRS"]] <- ggplot(data = 
                                                                                     temp_plot_data,aes(x=BPRS_TOT.x,y=correct_encoding_delay))+
  geom_point()+
  stat_smooth(method="lm")+
  ylab("Similarity")+
  ggtitle("Template encoding/delay vs BPRS")+
  theme_classic()

encoding_to_delay_plots[["correct_encoding_to_correct_delay"]][["L3_Acc"]] <- ggplot(data = 
                                                                                       temp_plot_data,aes(x=XDFR_MRI_ACC_L3,y=correct_encoding_delay))+
  geom_point()+
  stat_smooth(method="lm")+
  ylab("Similarity")+
  ggtitle("Template encoding/delay vs L3 accuracy")+
  theme_classic()

In these, graphs, if a correlation is black, it is below p < 0.05; if it is not, it is grey.

correlations = list()

for (i in c(6,10,12)){
  colnames(similarity_temp[[i]]) <- unlist(similarity_temp[[1]][[1]])
  temp_list <- list(r = data.frame(matrix(nrow=4,ncol=6)), p = data.frame(matrix(nrow=4,ncol=6)))
  
  for (behav in seq.int(2,7)){
    for (sim in seq.int(1,4)){ 
      temp_corr <- cor.test(similarity_temp[[i]][,sim],data_to_plot[,behav])
      temp_list[["r"]][sim,behav-1] <- temp_corr$estimate
      temp_list[["p"]][sim,behav-1] <- temp_corr$p.value
    }
  }
  
  colnames(temp_list[["r"]]) <- colnames(data_to_plot)[2:7]
  rownames(temp_list[["r"]]) <- colnames(similarity_temp[[i]])
  colnames(temp_list[["p"]]) <- colnames(data_to_plot)[2:7]
  rownames(temp_list[["p"]]) <- colnames(similarity_temp[[i]])
  
  
  correlations[[names(similarity_temp)[i]]] <- temp_list
  
}
temp <- data.frame(r=matrix(nrow=6,ncol=1),p=matrix(nrow=6,ncol=1))
rownames(temp) <- colnames(data_to_plot)[2:7]


for (behav in seq.int(2,7)){
  temp_corr <- cor.test(similarity_temp[["correct_encoding_to_correct_delay"]],data_to_plot[,behav])
  temp$r[behav-1] <- temp_corr$estimate
  temp$p[behav-2] <- temp_corr$p.value
}

correlations[["correct_encoding_to_correct_delay"]] <- temp

Individual Encoding to Individual Delay

Here, taking the correlation between multivariate representation at encoding (TR 5) and delay (TR 8) on each trial.

This is definitely the messiest - there seem to be slight negative correlations between omnibus span and similiarity in correct trials (though this is not statistically significant), and definitely a negative correlation between inter-trial similarity at high load and accuracy. Also interesting to see (though not shown here, is that high load accuracy only correlates with similarity at high load, and low load accuracy only correlates to similarity at low load) Both of these suggest that the less similiar encoding and delay period are, the higher capacity and better performance an individual has, particularly on high load trials.

correlations[["encoding_to_delay_avg"]][["r"]] %>% 
  mutate(
    condition = row.names(.),
    omnibus_span_no_DFR_MRI = cell_spec(omnibus_span_no_DFR_MRI, "html", 
                                        color =ifelse(correlations[["encoding_to_delay_avg"]][["p"]]$omnibus_span_no_DFR_MRI < 0.05, "black", "grey")),
    XDFR_MRI_ACC_L3 = cell_spec(XDFR_MRI_ACC_L3, "html", 
                                color =ifelse(correlations[["encoding_to_delay_avg"]][["p"]]$XDFR_MRI_ACC_L3 < 0.05, "black", "grey")),
    BPRS_TOT.x = cell_spec(BPRS_TOT.x, "html", 
                           color =ifelse(correlations[["encoding_to_delay_avg"]][["p"]]$BPRS_TOT.x < 0.05, "black", "grey"))
  ) %>% 
  select(condition,omnibus_span_no_DFR_MRI,XDFR_MRI_ACC_L3,BPRS_TOT.x) %>%
  kable(format = "html", escape = F) %>%
  kable_styling("striped", full_width = F) %>% 
  add_header_above((c(" ", "Individual Encoding to Individual Delay" = 3)))

	Individual Encoding to Individual Delay
condition	omnibus_span_no_DFR_MRI	XDFR_MRI_ACC_L3	BPRS_TOT.x
low load incorrect	0.0242637091035295	0.0232742994042766	-0.123810779509404
high load incorrect	0.0196214074574858	-0.166333549374184	0.0269010595004991
low load correct	-0.0138011463724424	-0.0884899423553457	-0.0874632578173017
high load correct	-0.081886675592525	-0.203089348295182	0.0389720246240788

An important check is to see whether these measures that we’re calculating have any behavioral relevance. To look at this, we’re going to run paired t-tests to look at whether we can distinguish load across correct trials and accuracy across high load trials for each of the measures we calculate.

This measure can’t distinguish either load or accuracy.

encoding_to_delay_avg <- fisherz(similarity_temp[["encoding_to_delay_avg"]])

t.test(encoding_to_delay_avg[,4],encoding_to_delay_avg[,2],paired=TRUE)

## 
##  Paired t-test
## 
## data:  encoding_to_delay_avg[, 4] and encoding_to_delay_avg[, 2]
## t = -0.91418, df = 169, p-value = 0.3619
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.02871061  0.01053602
## sample estimates:
## mean of the differences 
##            -0.009087295

t.test(encoding_to_delay_avg[,4],encoding_to_delay_avg[,3],paired=TRUE)

## 
##  Paired t-test
## 
## data:  encoding_to_delay_avg[, 4] and encoding_to_delay_avg[, 3]
## t = -0.68679, df = 169, p-value = 0.4932
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.03262912  0.01578560
## sample estimates:
## mean of the differences 
##            -0.008421763

(encoding_to_delay_plots[["encoding_to_delay_avg"]][["omnibus"]][["high_load_correct"]] + encoding_to_delay_plots[["encoding_to_delay_avg"]][["omnibus"]][["high_load_incorrect"]]) /
  (encoding_to_delay_plots[["encoding_to_delay_avg"]][["omnibus"]][["low_load_correct"]] + 
     encoding_to_delay_plots[["encoding_to_delay_avg"]][["omnibus"]][["low_load_incorrect"]])+
  plot_annotation(title = "Individual trials vs omnibus")

## Warning: Removed 57 rows containing non-finite values (stat_smooth).

## Warning: Removed 57 rows containing missing values (geom_point).

(encoding_to_delay_plots[["encoding_to_delay_avg"]][["L3_Acc"]][["high_load_correct"]] + encoding_to_delay_plots[["encoding_to_delay_avg"]][["L3_Acc"]][["high_load_incorrect"]]) /
  (encoding_to_delay_plots[["encoding_to_delay_avg"]][["L3_Acc"]][["low_load_correct"]] + 
     encoding_to_delay_plots[["encoding_to_delay_avg"]][["L3_Acc"]][["low_load_incorrect"]])+
  plot_annotation(title = "Individual trials vs High Load Accuracy")

## Warning: Removed 57 rows containing non-finite values (stat_smooth).

## Warning: Removed 57 rows containing missing values (geom_point).

(encoding_to_delay_plots[["encoding_to_delay_avg"]][["BPRS"]][["high_load_correct"]] + encoding_to_delay_plots[["encoding_to_delay_avg"]][["BPRS"]][["high_load_incorrect"]]) /
  (encoding_to_delay_plots[["encoding_to_delay_avg"]][["BPRS"]][["low_load_correct"]] + 
     encoding_to_delay_plots[["encoding_to_delay_avg"]][["BPRS"]][["low_load_incorrect"]])+
  plot_annotation(title = "Individual trials vs BPRS Total")

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 58 rows containing non-finite values (stat_smooth).

## Warning: Removed 58 rows containing missing values (geom_point).

Individual Trial Encoding vs Correct Template Delay

Here, we’re taking the delay period from a “template” (ie average of all correct trials) and correlating that to the encoding period of each trial. We see strong negative correlations with accuracy for all trial types except for low load incorrect trials, and positive correlations with BPRS for the high load trials (regardless of accuracy). This suggests that as there is stronger correlation between encoding on a given trial and the correct delay template, there are more psychiatric symptoms.

The relationship between omnibus span and similarity in the correct trials (irrespective of load) is trending towards significance.

correlations[["encoding_to_correct_delay_avg"]][["r"]] %>% 
  mutate(
    condition = row.names(.),
    omnibus_span_no_DFR_MRI = cell_spec(omnibus_span_no_DFR_MRI, "html", 
                                        color =ifelse(correlations[["encoding_to_correct_delay_avg"]][["p"]]$omnibus_span_no_DFR_MRI < 0.05, "black", "grey")),
    XDFR_MRI_ACC_L3 = cell_spec(XDFR_MRI_ACC_L3, "html", 
                                color =ifelse(correlations[["encoding_to_correct_delay_avg"]][["p"]]$XDFR_MRI_ACC_L3 < 0.05, "black", "grey")),
    BPRS_TOT.x = cell_spec(BPRS_TOT.x, "html", 
                           color =ifelse(correlations[["encoding_to_correct_delay_avg"]][["p"]]$BPRS_TOT.x < 0.05, "black", "grey"))
  ) %>% 
  select(condition,omnibus_span_no_DFR_MRI,XDFR_MRI_ACC_L3,BPRS_TOT.x) %>%
  kable(format = "html", escape = F) %>%
  kable_styling("striped", full_width = F) %>% 
  add_header_above((c(" ", "Individual Encoding to Template Delay" = 3)))

	Individual Encoding to Template Delay
condition	omnibus_span_no_DFR_MRI	XDFR_MRI_ACC_L3	BPRS_TOT.x
low load incorrect	-0.143253325182348	-0.219430855781915	0.135882061706973
high load incorrect	-0.0715837210736226	-0.194870744630673	0.185599361045007
low load correct	-0.137040126258098	-0.337025669157246	0.187409373936538
high load correct	-0.14181434005323	-0.342023439290818	0.192604510469676

This measure distinguishes both load and accuracy.

encoding_to_correct_delay_avg <- fisherz(similarity_temp[["encoding_to_correct_delay_avg"]])

t.test(encoding_to_correct_delay_avg[,4],encoding_to_correct_delay_avg[,2],paired=TRUE)

## 
##  Paired t-test
## 
## data:  encoding_to_correct_delay_avg[, 4] and encoding_to_correct_delay_avg[, 2]
## t = -3.9205, df = 169, p-value = 0.0001282
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.04637851 -0.01531431
## sample estimates:
## mean of the differences 
##             -0.03084641

t.test(encoding_to_correct_delay_avg[,4],encoding_to_correct_delay_avg[,3],paired=TRUE)

## 
##  Paired t-test
## 
## data:  encoding_to_correct_delay_avg[, 4] and encoding_to_correct_delay_avg[, 3]
## t = -12.307, df = 169, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.10590627 -0.07662617
## sample estimates:
## mean of the differences 
##             -0.09126622

(encoding_to_delay_plots[["encoding_to_correct_delay_avg"]][["omnibus"]][["high_load_correct"]] + encoding_to_delay_plots[["encoding_to_correct_delay_avg"]][["omnibus"]][["high_load_incorrect"]]) /
  (encoding_to_delay_plots[["encoding_to_correct_delay_avg"]][["omnibus"]][["low_load_correct"]] + 
     encoding_to_delay_plots[["encoding_to_correct_delay_avg"]][["omnibus"]][["low_load_incorrect"]])+
  plot_annotation(title = "Individual trials vs omnibus")

## Warning: Removed 57 rows containing non-finite values (stat_smooth).

## Warning: Removed 57 rows containing missing values (geom_point).

(encoding_to_delay_plots[["encoding_to_correct_delay_avg"]][["L3_Acc"]][["high_load_correct"]] + encoding_to_delay_plots[["encoding_to_correct_delay_avg"]][["L3_Acc"]][["high_load_incorrect"]]) /
  (encoding_to_delay_plots[["encoding_to_correct_delay_avg"]][["L3_Acc"]][["low_load_correct"]] + 
     encoding_to_delay_plots[["encoding_to_correct_delay_avg"]][["L3_Acc"]][["low_load_incorrect"]])+
  plot_annotation(title = "Individual trials vs High Load Accuracy")

## Warning: Removed 57 rows containing non-finite values (stat_smooth).

## Warning: Removed 57 rows containing missing values (geom_point).

(encoding_to_delay_plots[["encoding_to_correct_delay_avg"]][["BPRS"]][["high_load_correct"]] + encoding_to_delay_plots[["encoding_to_correct_delay_avg"]][["BPRS"]][["high_load_incorrect"]]) /
  (encoding_to_delay_plots[["encoding_to_correct_delay_avg"]][["BPRS"]][["low_load_correct"]] + 
     encoding_to_delay_plots[["encoding_to_correct_delay_avg"]][["BPRS"]][["low_load_incorrect"]])+
  plot_annotation(title = "Individual trials vs BPRS Total")

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 58 rows containing non-finite values (stat_smooth).

## Warning: Removed 58 rows containing missing values (geom_point).

We’ve seen that there is a relationship between BPRS and the correlation between the individual encoding trial and the template delay, but we want to make sure it holds when we control for accuracy and span. Both of them still do!

data_for_reg <- data.frame(acc = data_to_plot$XDFR_MRI_ACC_L3,span = data_to_plot$omnibus_span_no_DFR_MRI, BPRS = data_to_plot$BPRS_TOT.x, ITC_correct = similarity_temp[["encoding_to_correct_delay_avg"]][,4], ITC_incorrect = similarity_temp[["encoding_to_correct_delay_avg"]][,2])

BPRS_correct.lm <- lm(BPRS ~ acc + span + ITC_correct,data = data_for_reg)
summary(BPRS_correct.lm)

## 
## Call:
## lm(formula = BPRS ~ acc + span + ITC_correct, data = data_for_reg)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -10.6649  -4.9112  -0.7108   3.6352  22.7267 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   37.565      4.104   9.153   <2e-16 ***
## acc           -1.465      5.791  -0.253   0.8006    
## span          -2.546      1.019  -2.498   0.0135 *  
## ITC_correct    7.981      3.936   2.028   0.0442 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.846 on 165 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.07646,    Adjusted R-squared:  0.05967 
## F-statistic: 4.554 on 3 and 165 DF,  p-value: 0.004294

BPRS_incorrect.lm <- lm(BPRS ~ acc + span + ITC_incorrect,data = data_for_reg)
summary(BPRS_incorrect.lm)

## 
## Call:
## lm(formula = BPRS ~ acc + span + ITC_incorrect, data = data_for_reg)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -11.8097  -5.1056  -0.6128   3.5993  21.3160 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     38.414      4.084   9.405   <2e-16 ***
## acc             -2.905      5.592  -0.520   0.6041    
## span            -2.602      1.017  -2.560   0.0114 *  
## ITC_incorrect    8.026      3.708   2.164   0.0319 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.835 on 165 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.07958,    Adjusted R-squared:  0.06285 
## F-statistic: 4.755 on 3 and 165 DF,  p-value: 0.003306

Correct Template Encoding vs Individual Trial Delay

Interestingly, we see a slightly different pattern when we look at how the delay period of any given trial correlates to the correct encoding template. Omnibus span is positively correlated with similarity in the low load incorrect trials (though caution, few trials here), and negatively correlated with the high load correct trials. Accuracy is correlated to similarity at high load trials and correct low load trials, and there are significant positive correlations between BPRS and high load correct trials.

correlations[["correct_encoding_to_delay_avg"]][["r"]] %>% 
  mutate(
    condition = row.names(.),
    omnibus_span_no_DFR_MRI = cell_spec(omnibus_span_no_DFR_MRI, "html", 
                                        color =ifelse(correlations[["correct_encoding_to_delay_avg"]][["p"]]$omnibus_span_no_DFR_MRI < 0.05, "black", "grey")),
    XDFR_MRI_ACC_L3 = cell_spec(XDFR_MRI_ACC_L3, "html", 
                                color =ifelse(correlations[["correct_encoding_to_delay_avg"]][["p"]]$XDFR_MRI_ACC_L3 < 0.05, "black", "grey")),
    BPRS_TOT.x = cell_spec(BPRS_TOT.x, "html", 
                           color =ifelse(correlations[["correct_encoding_to_delay_avg"]][["p"]]$BPRS_TOT.x < 0.05, "black", "grey"))
  ) %>% 
  select(condition,omnibus_span_no_DFR_MRI,XDFR_MRI_ACC_L3,BPRS_TOT.x) %>%
  kable(format = "html", escape = F) %>%
  kable_styling("striped", full_width = F) %>% 
  add_header_above((c(" ", "Template Encoding to Delay" = 3)))

	Template Encoding to Delay
condition	omnibus_span_no_DFR_MRI	XDFR_MRI_ACC_L3	BPRS_TOT.x
low load incorrect	0.142568841398427	0.0488176325213739	-0.0421605373911986
high load incorrect	-0.0449086927965139	-0.236684645554992	0.0969031173550461
low load correct	-0.137395065457336	-0.261330225459152	0.135331930734418
high load correct	-0.183668322006186	-0.293690787439439	0.26670467177783

This measure distinguishes both load and accuracy.

correct_encoding_to_delay_avg <- fisherz(similarity_temp[["correct_encoding_to_delay_avg"]])

t.test(correct_encoding_to_delay_avg[,4],correct_encoding_to_delay_avg[,2],paired=TRUE)

## 
##  Paired t-test
## 
## data:  correct_encoding_to_delay_avg[, 4] and correct_encoding_to_delay_avg[, 2]
## t = -3.1517, df = 169, p-value = 0.001921
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.041176278 -0.009459724
## sample estimates:
## mean of the differences 
##               -0.025318

t.test(correct_encoding_to_delay_avg[,4],correct_encoding_to_delay_avg[,3],paired=TRUE)

## 
##  Paired t-test
## 
## data:  correct_encoding_to_delay_avg[, 4] and correct_encoding_to_delay_avg[, 3]
## t = 3.6653, df = 169, p-value = 0.0003305
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  0.01071411 0.03572743
## sample estimates:
## mean of the differences 
##              0.02322077

(encoding_to_delay_plots[["correct_encoding_to_delay_avg"]][["omnibus"]][["high_load_correct"]] + encoding_to_delay_plots[["correct_encoding_to_delay_avg"]][["omnibus"]][["high_load_incorrect"]]) /
  (encoding_to_delay_plots[["correct_encoding_to_delay_avg"]][["omnibus"]][["low_load_correct"]] + 
     encoding_to_delay_plots[["correct_encoding_to_delay_avg"]][["omnibus"]][["low_load_incorrect"]])+
  plot_annotation(title = "Individual trials vs omnibus")

## Warning: Removed 57 rows containing non-finite values (stat_smooth).

## Warning: Removed 57 rows containing missing values (geom_point).

(encoding_to_delay_plots[["correct_encoding_to_delay_avg"]][["L3_Acc"]][["high_load_correct"]] + encoding_to_delay_plots[["correct_encoding_to_delay_avg"]][["L3_Acc"]][["high_load_incorrect"]]) /
  (encoding_to_delay_plots[["correct_encoding_to_delay_avg"]][["L3_Acc"]][["low_load_correct"]] + 
     encoding_to_delay_plots[["correct_encoding_to_delay_avg"]][["L3_Acc"]][["low_load_incorrect"]])+
  plot_annotation(title = "Individual trials vs High Load Accuracy")

## Warning: Removed 57 rows containing non-finite values (stat_smooth).

## Warning: Removed 57 rows containing missing values (geom_point).

(encoding_to_delay_plots[["correct_encoding_to_delay_avg"]][["BPRS"]][["high_load_correct"]] + encoding_to_delay_plots[["correct_encoding_to_delay_avg"]][["BPRS"]][["high_load_incorrect"]]) /
  (encoding_to_delay_plots[["correct_encoding_to_delay_avg"]][["BPRS"]][["low_load_correct"]] + 
     encoding_to_delay_plots[["correct_encoding_to_delay_avg"]][["BPRS"]][["low_load_incorrect"]])+
  plot_annotation(title = "Individual trials vs BPRS Total")

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 58 rows containing non-finite values (stat_smooth).

## Warning: Removed 58 rows containing missing values (geom_point).

This one also does!

data_for_reg <- data.frame(acc = data_to_plot$XDFR_MRI_ACC_L3,span = data_to_plot$omnibus_span_no_DFR_MRI, BPRS = data_to_plot$BPRS_TOT.x, ITC_correct = similarity_temp[["correct_encoding_to_delay_avg"]][,4], ITC_incorrect = similarity_temp[["correct_encoding_to_delay_avg"]][,2])

BPRS_correct.lm <- lm(BPRS ~ acc + span + ITC_correct,data = data_for_reg)
summary(BPRS_correct.lm)

## 
## Call:
## lm(formula = BPRS ~ acc + span + ITC_correct, data = data_for_reg)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -11.2373  -5.0868  -0.6637   3.0361  23.2875 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  37.6086     4.0400   9.309  < 2e-16 ***
## acc          -0.6422     5.6362  -0.114  0.90943    
## span         -2.2965     1.0107  -2.272  0.02436 *  
## ITC_correct  15.8865     5.3436   2.973  0.00339 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.752 on 165 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.1016, Adjusted R-squared:  0.08524 
## F-statistic: 6.218 on 3 and 165 DF,  p-value: 0.0005007

Correct template encoding to correct template delay

Here, we just want to look at the correlation between the template encoding to template delay. In this analysis, we see significant negative correlation with omnibus span, negative correlation with accuracy, and a positive correlation with BPRS.

These relationships suggest that as span and accuracy increase, there is less similarity beween encoding and delay, but we see the opposite relationship with psychiatric symptoms.

correlations[["correct_encoding_to_correct_delay"]][c(2,3,5),] %>% 
  mutate(
    condition = row.names(.),
    r = cell_spec(r, "html", 
                  color =ifelse(p < 0.05, "black", "grey"))
  )  %>% 
  select(condition,r) %>%
  kable(format = "html", escape = F) %>%
  kable_styling("striped", full_width = F) %>%
  add_header_above(c(" ", "Template Encoding to Template Delay" = 1))

	Template Encoding to Template Delay
condition	r
omnibus_span_no_DFR_MRI	-0.162994555337693
XDFR_MRI_ACC_L3	-0.276758447584082
BPRS_TOT.x	0.20039919415834

encoding_to_delay_plots[["correct_encoding_to_correct_delay"]][["omnibus"]]

encoding_to_delay_plots[["correct_encoding_to_correct_delay"]][["L3_Acc"]]

encoding_to_delay_plots[["correct_encoding_to_correct_delay"]][["BPRS"]]

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

This one, however, does not.

data_for_reg <- data.frame(acc = data_to_plot$XDFR_MRI_ACC_L3,span = data_to_plot$omnibus_span_no_DFR_MRI, BPRS = data_to_plot$BPRS_TOT.x, ITC = similarity_temp[["correct_encoding_to_correct_delay"]])

BPRS.lm <- lm(BPRS ~ ITC + span + acc,data = data_for_reg)
summary(BPRS.lm)

## 
## Call:
## lm(formula = BPRS ~ ITC + span + acc, data = data_for_reg)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -10.639  -4.930  -1.025   3.631  23.395 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   38.241      4.089   9.352   <2e-16 ***
## ITC            3.973      1.933   2.055   0.0415 *  
## span          -2.435      1.023  -2.381   0.0184 *  
## acc           -2.110      5.695  -0.371   0.7114    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.844 on 165 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.07706,    Adjusted R-squared:  0.06028 
## F-statistic: 4.592 on 3 and 165 DF,  p-value: 0.004084

Comparing encoding to probe

Another theoretically interesting period is comparing encoding to probe. Here, we’ll do the same thing as above, but we’re going to take from TR 5 for encoding and TR 11 for probe.

encoding_to_probe_plots <- list()

for (i in c(7,11,13)){
  colnames(similarity_temp[[i]]) <- unlist(similarity_temp[[1]][[1]])
  similarity_temp[[i]][similarity_temp[[i]]==0] <- NA
  temp_plot_data <- cbind.data.frame(data_to_plot,similarity_temp[[i]])
  
  encoding_to_probe_plots[[names(similarity_temp)[i]]][["omnibus"]][["low_load_incorrect"]] <- ggplot(data = temp_plot_data,aes(x=omnibus_span_no_DFR_MRI,y=`low load incorrect`))+
    geom_point()+
    stat_smooth(method="lm")+
    ylab("Similarity")+
    ggtitle("Low Load - Incorrect trials")+
    theme_classic()
  
  encoding_to_probe_plots[[names(similarity_temp)[i]]][["omnibus"]][["high_load_incorrect"]] <- ggplot(data = temp_plot_data,aes(x=omnibus_span_no_DFR_MRI,y=`high load incorrect`))+
    geom_point()+
    stat_smooth(method="lm")+
    ylab("Similarity")+
    ggtitle("High Load - Incorrect trials")+
    theme_classic()
  
  encoding_to_probe_plots[[names(similarity_temp)[i]]][["omnibus"]][["low_load_correct"]] <- ggplot(data = temp_plot_data,aes(x=omnibus_span_no_DFR_MRI,y=`low load correct`))+
    geom_point()+
    stat_smooth(method="lm")+
    ylab("Similarity")+
    ggtitle("Low Load - Correct trials")+
    theme_classic()
  
  encoding_to_probe_plots[[names(similarity_temp)[i]]][["omnibus"]][["high_load_correct"]] <- ggplot(data = temp_plot_data,aes(x=omnibus_span_no_DFR_MRI,y=`high load correct`))+
    geom_point()+
    stat_smooth(method="lm")+
    ylab("Similarity")+
    ggtitle("High Load - Correct trials")+
    theme_classic()
  
  encoding_to_probe_plots[[names(similarity_temp)[i]]][["L3_Acc"]][["low_load_incorrect"]] <- ggplot(data = temp_plot_data,aes(x=XDFR_MRI_ACC_L3,y=`low load incorrect`))+
    geom_point()+
    stat_smooth(method="lm")+
    ylab("Similarity")+
    ggtitle("Low Load - Incorrect trials")+
    theme_classic()
  
  encoding_to_probe_plots[[names(similarity_temp)[i]]][["L3_Acc"]][["high_load_incorrect"]] <- ggplot(data = temp_plot_data,aes(x=XDFR_MRI_ACC_L3,y=`high load incorrect`))+
    geom_point()+
    stat_smooth(method="lm")+
    ylab("Similarity")+
    ggtitle("High Load - Incorrect trials")+
    theme_classic()
  
  encoding_to_probe_plots[[names(similarity_temp)[i]]][["L3_Acc"]][["low_load_correct"]] <- ggplot(data = temp_plot_data,aes(x=XDFR_MRI_ACC_L3,y=`low load correct`))+
    geom_point()+
    stat_smooth(method="lm")+
    ylab("Similarity")+
    ggtitle("Low Load - Correct trials")+
    theme_classic()
  
  encoding_to_probe_plots[[names(similarity_temp)[i]]][["L3_Acc"]][["high_load_correct"]] <- ggplot(data = temp_plot_data,aes(x=XDFR_MRI_ACC_L3,y=`high load correct`))+
    geom_point()+
    stat_smooth(method="lm")+
    ylab("Similarity")+
    ggtitle("High Load - Correct trials")+
    theme_classic()
  
  encoding_to_probe_plots[[names(similarity_temp)[i]]][["BPRS"]][["low_load_incorrect"]] <- ggplot(data = temp_plot_data,aes(x=BPRS_TOT.x,y=`low load incorrect`))+
    geom_point()+
    stat_smooth(method="lm")+
    ylab("Similarity")+
    ggtitle("Low Load - Incorrect trials")+
    theme_classic()
  
  encoding_to_probe_plots[[names(similarity_temp)[i]]][["BPRS"]][["high_load_incorrect"]] <- ggplot(data = temp_plot_data,aes(x=BPRS_TOT.x,y=`high load incorrect`))+
    geom_point()+
    stat_smooth(method="lm")+
    ylab("Similarity")+
    ggtitle("High Load - Incorrect trials")+
    theme_classic()
  
  encoding_to_probe_plots[[names(similarity_temp)[i]]][["BPRS"]][["low_load_correct"]] <- ggplot(data = temp_plot_data,aes(x=BPRS_TOT.x,y=`low load correct`))+
    geom_point()+
    stat_smooth(method="lm")+
    ylab("Similarity")+
    ggtitle("Low Load - Correct trials")+
    theme_classic()
  
  encoding_to_probe_plots[[names(similarity_temp)[i]]][["BPRS"]][["high_load_correct"]] <- ggplot(data = temp_plot_data,aes(x=BPRS_TOT.x,y=`high load correct`))+
    geom_point()+
    stat_smooth(method="lm")+
    ylab("Similarity")+
    ggtitle("High Load - Correct trials")+
    theme_classic()
}

temp_plot_data <- cbind.data.frame(data_to_plot,similarity_temp[["correct_encoding_to_correct_probe"]])
colnames(temp_plot_data)[9] <- "correct_encoding_probe"

encoding_to_probe_plots[["correct_encoding_to_correct_probe"]][["omnibus"]] <- ggplot(data = 
                                                                                        temp_plot_data,aes(x=omnibus_span_no_DFR_MRI,y=correct_encoding_probe))+
  geom_point()+
  stat_smooth(method="lm")+
  ylab("Similarity")+
  ggtitle("Template encoding/probe vs Omnibus Span")+
  theme_classic()

encoding_to_probe_plots[["correct_encoding_to_correct_probe"]][["BPRS"]] <- ggplot(data = 
                                                                                     temp_plot_data,aes(x=BPRS_TOT.x,y=correct_encoding_probe))+
  geom_point()+
  stat_smooth(method="lm")+
  ylab("Similarity")+
  ggtitle("Template encoding/probe vs BPRS")+
  theme_classic()

encoding_to_probe_plots[["correct_encoding_to_correct_probe"]][["L3_Acc"]] <- ggplot(data = 
                                                                                       temp_plot_data,aes(x=XDFR_MRI_ACC_L3,y=correct_encoding_probe))+
  geom_point()+
  stat_smooth(method="lm")+
  ylab("Similarity")+
  ggtitle("Template encoding/probe vs L3 accuracy")+
  theme_classic()

In these, graphs, if a correlation is black, it is below p < 0.05; if it is not, it is grey.

correlations = list()

for (i in c(7,11,13)){
  colnames(similarity_temp[[i]]) <- unlist(similarity_temp[[1]][[1]])
  temp_list <- list(r = data.frame(matrix(nrow=4,ncol=6)), p = data.frame(matrix(nrow=4,ncol=6)))
  
  for (behav in seq.int(2,7)){
    for (sim in seq.int(1,4)){ 
      temp_corr <- cor.test(similarity_temp[[i]][,sim],data_to_plot[,behav])
      temp_list[["r"]][sim,behav-1] <- temp_corr$estimate
      temp_list[["p"]][sim,behav-1] <- temp_corr$p.value
    }
  }
  
  colnames(temp_list[["r"]]) <- colnames(data_to_plot)[2:7]
  rownames(temp_list[["r"]]) <- colnames(similarity_temp[[i]])
  colnames(temp_list[["p"]]) <- colnames(data_to_plot)[2:7]
  rownames(temp_list[["p"]]) <- colnames(similarity_temp[[i]])
  
  
  correlations[[names(similarity_temp)[i]]] <- temp_list
  
}
temp <- data.frame(r=matrix(nrow=6,ncol=1),p=matrix(nrow=6,ncol=1))
rownames(temp) <- colnames(data_to_plot)[2:7]


for (behav in seq.int(2,7)){
  temp_corr <- cor.test(similarity_temp[["correct_encoding_to_correct_probe"]],data_to_plot[,behav])
  temp$r[behav-1] <- temp_corr$estimate
  temp$p[behav-2] <- temp_corr$p.value
}

correlations[["correct_encoding_to_correct_probe"]] <- temp

Individual Encoding to Individual Probe

Here, taking the correlation between multivariate representation at encoding (TR 5) and probe (TR 11) on each trial.

The only significant correlation we see here is between accuracy an high load correct trials.

correlations[["encoding_to_probe_avg"]][["r"]] %>% 
  mutate(
    condition = row.names(.),
    omnibus_span_no_DFR_MRI = cell_spec(omnibus_span_no_DFR_MRI, "html", 
                                        color =ifelse(correlations[["encoding_to_probe_avg"]][["p"]]$omnibus_span_no_DFR_MRI < 0.05, "black", "grey")),
    XDFR_MRI_ACC_L3 = cell_spec(XDFR_MRI_ACC_L3, "html", 
                                color =ifelse(correlations[["encoding_to_probe_avg"]][["p"]]$XDFR_MRI_ACC_L3 < 0.05, "black", "grey")),
    BPRS_TOT.x = cell_spec(BPRS_TOT.x, "html", 
                           color =ifelse(correlations[["encoding_to_probe_avg"]][["p"]]$BPRS_TOT.x < 0.05, "black", "grey"))
  ) %>% 
  select(condition,omnibus_span_no_DFR_MRI,XDFR_MRI_ACC_L3,BPRS_TOT.x) %>%
  kable(format = "html", escape = F) %>%
  kable_styling("striped", full_width = F) %>% 
  add_header_above((c(" ", "Individual Encoding to Individual Probe" = 3)))

	Individual Encoding to Individual Probe
condition	omnibus_span_no_DFR_MRI	XDFR_MRI_ACC_L3	BPRS_TOT.x
low load incorrect	-0.107958873031196	0.135077934527628	-0.00271329088133755
high load incorrect	-0.063901040567794	0.114113255823614	-0.107076328962866
low load correct	-0.148308000737086	0.100572367678891	-0.0155467140238554
high load correct	0.0398996225962218	0.189082168572699	-0.0404386802904891

This measure can’t distinguish between load but not accuracy (but it’s very very close).

encoding_to_probe_avg <- fisherz(similarity_temp[["encoding_to_probe_avg"]])

t.test(encoding_to_probe_avg[,4],encoding_to_probe_avg[,2],paired=TRUE)

## 
##  Paired t-test
## 
## data:  encoding_to_probe_avg[, 4] and encoding_to_probe_avg[, 2]
## t = 1.9688, df = 169, p-value = 0.05061
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -4.648305e-05  3.445211e-02
## sample estimates:
## mean of the differences 
##              0.01720281

t.test(encoding_to_probe_avg[,4],encoding_to_probe_avg[,3],paired=TRUE)

## 
##  Paired t-test
## 
## data:  encoding_to_probe_avg[, 4] and encoding_to_probe_avg[, 3]
## t = 2.7727, df = 169, p-value = 0.006183
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  0.004980572 0.029603550
## sample estimates:
## mean of the differences 
##              0.01729206

(encoding_to_probe_plots[["encoding_to_probe_avg"]][["omnibus"]][["high_load_correct"]] + encoding_to_probe_plots[["encoding_to_probe_avg"]][["omnibus"]][["high_load_incorrect"]]) /
  (encoding_to_probe_plots[["encoding_to_probe_avg"]][["omnibus"]][["low_load_correct"]] + 
     encoding_to_probe_plots[["encoding_to_probe_avg"]][["omnibus"]][["low_load_incorrect"]])+
  plot_annotation(title = "Individual trials vs omnibus")

## Warning: Removed 57 rows containing non-finite values (stat_smooth).

## Warning: Removed 57 rows containing missing values (geom_point).

(encoding_to_probe_plots[["encoding_to_probe_avg"]][["L3_Acc"]][["high_load_correct"]] + encoding_to_probe_plots[["encoding_to_probe_avg"]][["L3_Acc"]][["high_load_incorrect"]]) /
  (encoding_to_probe_plots[["encoding_to_probe_avg"]][["L3_Acc"]][["low_load_correct"]] + 
     encoding_to_probe_plots[["encoding_to_probe_avg"]][["L3_Acc"]][["low_load_incorrect"]])+
  plot_annotation(title = "Individual trials vs High Load Accuracy")

## Warning: Removed 57 rows containing non-finite values (stat_smooth).

## Warning: Removed 57 rows containing missing values (geom_point).

(encoding_to_probe_plots[["encoding_to_probe_avg"]][["BPRS"]][["high_load_correct"]] + encoding_to_probe_plots[["encoding_to_probe_avg"]][["BPRS"]][["high_load_incorrect"]]) /
  (encoding_to_probe_plots[["encoding_to_probe_avg"]][["BPRS"]][["low_load_correct"]] + 
     encoding_to_probe_plots[["encoding_to_probe_avg"]][["BPRS"]][["low_load_incorrect"]])+
  plot_annotation(title = "Individual trials vs BPRS Total")

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 58 rows containing non-finite values (stat_smooth).

## Warning: Removed 58 rows containing missing values (geom_point).

Individual Trial Encoding vs Correct Template Probe

This shows more relationships with accuracy - for all trial types except for low load incorrect.

correlations[["encoding_to_correct_probe_avg"]][["r"]] %>% 
  mutate(
    condition = row.names(.),
    omnibus_span_no_DFR_MRI = cell_spec(omnibus_span_no_DFR_MRI, "html", 
                                        color =ifelse(correlations[["encoding_to_correct_probe_avg"]][["p"]]$omnibus_span_no_DFR_MRI < 0.05, "black", "grey")),
    XDFR_MRI_ACC_L3 = cell_spec(XDFR_MRI_ACC_L3, "html", 
                                color =ifelse(correlations[["encoding_to_correct_probe_avg"]][["p"]]$XDFR_MRI_ACC_L3 < 0.05, "black", "grey")),
    BPRS_TOT.x = cell_spec(BPRS_TOT.x, "html", 
                           color =ifelse(correlations[["encoding_to_correct_probe_avg"]][["p"]]$BPRS_TOT.x < 0.05, "black", "grey"))
  ) %>% 
  select(condition,omnibus_span_no_DFR_MRI,XDFR_MRI_ACC_L3,BPRS_TOT.x) %>%
  kable(format = "html", escape = F) %>%
  kable_styling("striped", full_width = F) %>% 
  add_header_above((c(" ", "Individual Encoding to Template probe" = 3)))

	Individual Encoding to Template probe
condition	omnibus_span_no_DFR_MRI	XDFR_MRI_ACC_L3	BPRS_TOT.x
low load incorrect	-0.0233014821883	0.180143228787264	-0.0596330938748161
high load incorrect	0.0295132539321085	0.191840755817728	-0.0751303711927123
low load correct	0.0675959888532484	0.211685012506652	-0.0865980630474684
high load correct	0.0809525819237514	0.188723796046559	-0.0721345840999969

This measure distinguishes both load and accuracy.

encoding_to_correct_probe_avg <- fisherz(similarity_temp[["encoding_to_correct_probe_avg"]])

t.test(encoding_to_correct_probe_avg[,4],encoding_to_correct_probe_avg[,2],paired=TRUE)

## 
##  Paired t-test
## 
## data:  encoding_to_correct_probe_avg[, 4] and encoding_to_correct_probe_avg[, 2]
## t = 2.5252, df = 169, p-value = 0.01248
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  0.004154446 0.033919793
## sample estimates:
## mean of the differences 
##              0.01903712

t.test(encoding_to_correct_probe_avg[,4],encoding_to_correct_probe_avg[,3],paired=TRUE)

## 
##  Paired t-test
## 
## data:  encoding_to_correct_probe_avg[, 4] and encoding_to_correct_probe_avg[, 3]
## t = 7.4381, df = 169, p-value = 4.91e-12
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  0.03863874 0.06655843
## sample estimates:
## mean of the differences 
##              0.05259859

(encoding_to_probe_plots[["encoding_to_correct_probe_avg"]][["omnibus"]][["high_load_correct"]] + encoding_to_probe_plots[["encoding_to_correct_probe_avg"]][["omnibus"]][["high_load_incorrect"]]) /
  (encoding_to_probe_plots[["encoding_to_correct_probe_avg"]][["omnibus"]][["low_load_correct"]] + 
     encoding_to_probe_plots[["encoding_to_correct_probe_avg"]][["omnibus"]][["low_load_incorrect"]])+
  plot_annotation(title = "Individual trials vs omnibus")

## Warning: Removed 57 rows containing non-finite values (stat_smooth).

## Warning: Removed 57 rows containing missing values (geom_point).

(encoding_to_probe_plots[["encoding_to_correct_probe_avg"]][["L3_Acc"]][["high_load_correct"]] + encoding_to_probe_plots[["encoding_to_correct_probe_avg"]][["L3_Acc"]][["high_load_incorrect"]]) /
  (encoding_to_probe_plots[["encoding_to_correct_probe_avg"]][["L3_Acc"]][["low_load_correct"]] + 
     encoding_to_probe_plots[["encoding_to_correct_probe_avg"]][["L3_Acc"]][["low_load_incorrect"]])+
  plot_annotation(title = "Individual trials vs High Load Accuracy")

## Warning: Removed 57 rows containing non-finite values (stat_smooth).

## Warning: Removed 57 rows containing missing values (geom_point).

(encoding_to_probe_plots[["encoding_to_correct_probe_avg"]][["BPRS"]][["high_load_correct"]] + encoding_to_probe_plots[["encoding_to_correct_probe_avg"]][["BPRS"]][["high_load_incorrect"]]) /
  (encoding_to_probe_plots[["encoding_to_correct_probe_avg"]][["BPRS"]][["low_load_correct"]] + 
     encoding_to_probe_plots[["encoding_to_correct_probe_avg"]][["BPRS"]][["low_load_incorrect"]])+
  plot_annotation(title = "Individual trials vs BPRS Total")

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 58 rows containing non-finite values (stat_smooth).

## Warning: Removed 58 rows containing missing values (geom_point).

Correct Template Encoding vs Individual Trial probe

Here, we’re back to only seeing a relationship with high load correct trials and accuracy.

correlations[["correct_encoding_to_probe_avg"]][["r"]] %>% 
  mutate(
    condition = row.names(.),
    omnibus_span_no_DFR_MRI = cell_spec(omnibus_span_no_DFR_MRI, "html", 
                                        color =ifelse(correlations[["correct_encoding_to_probe_avg"]][["p"]]$omnibus_span_no_DFR_MRI < 0.05, "black", "grey")),
    XDFR_MRI_ACC_L3 = cell_spec(XDFR_MRI_ACC_L3, "html", 
                                color =ifelse(correlations[["correct_encoding_to_probe_avg"]][["p"]]$XDFR_MRI_ACC_L3 < 0.05, "black", "grey")),
    BPRS_TOT.x = cell_spec(BPRS_TOT.x, "html", 
                           color =ifelse(correlations[["correct_encoding_to_probe_avg"]][["p"]]$BPRS_TOT.x < 0.05, "black", "grey"))
  ) %>% 
  select(condition,omnibus_span_no_DFR_MRI,XDFR_MRI_ACC_L3,BPRS_TOT.x) %>%
  kable(format = "html", escape = F) %>%
  kable_styling("striped", full_width = F) %>% 
  add_header_above((c(" ", "Template Encoding to probe" = 3)))

	Template Encoding to probe
condition	omnibus_span_no_DFR_MRI	XDFR_MRI_ACC_L3	BPRS_TOT.x
low load incorrect	-0.0762342413747194	0.0290332248955645	0.0646720541050286
high load incorrect	-0.0175160242282491	0.0684533729427094	-0.0938632520843353
low load correct	0.0252641685058543	0.114685238680304	-0.0164848173216513
high load correct	0.0144723653941234	0.236736488996031	-0.0475945780836705

This measure only distinguishes load.

correct_encoding_to_probe_avg <- fisherz(similarity_temp[["correct_encoding_to_probe_avg"]])

t.test(correct_encoding_to_probe_avg[,4],correct_encoding_to_probe_avg[,2],paired=TRUE)

## 
##  Paired t-test
## 
## data:  correct_encoding_to_probe_avg[, 4] and correct_encoding_to_probe_avg[, 2]
## t = 1.3892, df = 169, p-value = 0.1666
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.004223028  0.024281031
## sample estimates:
## mean of the differences 
##                0.010029

t.test(correct_encoding_to_probe_avg[,4],correct_encoding_to_probe_avg[,3],paired=TRUE)

## 
##  Paired t-test
## 
## data:  correct_encoding_to_probe_avg[, 4] and correct_encoding_to_probe_avg[, 3]
## t = -2.4008, df = 169, p-value = 0.01744
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.025937268 -0.002529988
## sample estimates:
## mean of the differences 
##             -0.01423363

(encoding_to_probe_plots[["correct_encoding_to_probe_avg"]][["omnibus"]][["high_load_correct"]] + encoding_to_probe_plots[["correct_encoding_to_probe_avg"]][["omnibus"]][["high_load_incorrect"]]) /
  (encoding_to_probe_plots[["correct_encoding_to_probe_avg"]][["omnibus"]][["low_load_correct"]] + 
     encoding_to_probe_plots[["correct_encoding_to_probe_avg"]][["omnibus"]][["low_load_incorrect"]])+
  plot_annotation(title = "Individual trials vs omnibus")

## Warning: Removed 57 rows containing non-finite values (stat_smooth).

## Warning: Removed 57 rows containing missing values (geom_point).

(encoding_to_probe_plots[["correct_encoding_to_probe_avg"]][["L3_Acc"]][["high_load_correct"]] + encoding_to_probe_plots[["correct_encoding_to_probe_avg"]][["L3_Acc"]][["high_load_incorrect"]]) /
  (encoding_to_probe_plots[["correct_encoding_to_probe_avg"]][["L3_Acc"]][["low_load_correct"]] + 
     encoding_to_probe_plots[["correct_encoding_to_probe_avg"]][["L3_Acc"]][["low_load_incorrect"]])+
  plot_annotation(title = "Individual trials vs High Load Accuracy")

## Warning: Removed 57 rows containing non-finite values (stat_smooth).

## Warning: Removed 57 rows containing missing values (geom_point).

(encoding_to_probe_plots[["correct_encoding_to_probe_avg"]][["BPRS"]][["high_load_correct"]] + encoding_to_probe_plots[["correct_encoding_to_probe_avg"]][["BPRS"]][["high_load_incorrect"]]) /
  (encoding_to_probe_plots[["correct_encoding_to_probe_avg"]][["BPRS"]][["low_load_correct"]] + 
     encoding_to_probe_plots[["correct_encoding_to_probe_avg"]][["BPRS"]][["low_load_incorrect"]])+
  plot_annotation(title = "Individual trials vs BPRS Total")

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 58 rows containing non-finite values (stat_smooth).

## Warning: Removed 58 rows containing missing values (geom_point).

Correct template encoding to correct template probe

Again, we’re only seeing relationships between the correlationi and with accuracy.

correlations[["correct_encoding_to_correct_probe"]][c(2,3,5),] %>% 
  mutate(
    condition = row.names(.),
    r = cell_spec(r, "html", 
                  color =ifelse(p < 0.05, "black", "grey"))
  )  %>% 
  select(condition,r) %>%
  kable(format = "html", escape = F) %>%
  kable_styling("striped", full_width = F) %>%
  add_header_above(c(" ", "Template Encoding to Template Probe" = 1))

	Template Encoding to Template Probe
condition	r
omnibus_span_no_DFR_MRI	0.0769216725339786
XDFR_MRI_ACC_L3	0.144330609485881
BPRS_TOT.x	-0.013932844644966

encoding_to_probe_plots[["correct_encoding_to_correct_probe"]][["omnibus"]]

encoding_to_probe_plots[["correct_encoding_to_correct_probe"]][["L3_Acc"]]

encoding_to_probe_plots[["correct_encoding_to_correct_probe"]][["BPRS"]]

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

Comparing delay to probe

Another theoretically interesting period is comparing delay to probe. Interestingly, it seems as though comparing representations here is only diagnostic of correct vs incorrect trials (not high vs low load), and also is related to overall accuracy.

delay_to_probe_plots <- list()

for (i in c(3,8,9)){
  colnames(similarity_temp[[i]]) <- unlist(similarity_temp[[1]][[1]])
  similarity_temp[[i]][similarity_temp[[i]]==0] <- NA
  temp_plot_data <- cbind.data.frame(data_to_plot,similarity_temp[[i]])
  
  delay_to_probe_plots[[names(similarity_temp)[i]]][["omnibus"]][["low_load_incorrect"]] <- ggplot(data = temp_plot_data,aes(x=omnibus_span_no_DFR_MRI,y=`low load incorrect`))+
    geom_point()+
    stat_smooth(method="lm")+
    ylab("Similarity")+
    ggtitle("Low Load - Incorrect trials")+
    theme_classic()
  
  delay_to_probe_plots[[names(similarity_temp)[i]]][["omnibus"]][["high_load_incorrect"]] <- ggplot(data = temp_plot_data,aes(x=omnibus_span_no_DFR_MRI,y=`high load incorrect`))+
    geom_point()+
    stat_smooth(method="lm")+
    ylab("Similarity")+
    ggtitle("High Load - Incorrect trials")+
    theme_classic()
  
  delay_to_probe_plots[[names(similarity_temp)[i]]][["omnibus"]][["low_load_correct"]] <- ggplot(data = temp_plot_data,aes(x=omnibus_span_no_DFR_MRI,y=`low load correct`))+
    geom_point()+
    stat_smooth(method="lm")+
    ylab("Similarity")+
    ggtitle("Low Load - Correct trials")+
    theme_classic()
  
  delay_to_probe_plots[[names(similarity_temp)[i]]][["omnibus"]][["high_load_correct"]] <- ggplot(data = temp_plot_data,aes(x=omnibus_span_no_DFR_MRI,y=`high load correct`))+
    geom_point()+
    stat_smooth(method="lm")+
    ylab("Similarity")+
    ggtitle("High Load - Correct trials")+
    theme_classic()
  
  delay_to_probe_plots[[names(similarity_temp)[i]]][["L3_Acc"]][["low_load_incorrect"]] <- ggplot(data = temp_plot_data,aes(x=XDFR_MRI_ACC_L3,y=`low load incorrect`))+
    geom_point()+
    stat_smooth(method="lm")+
    ylab("Similarity")+
    ggtitle("Low Load - Incorrect trials")+
    theme_classic()
  
  delay_to_probe_plots[[names(similarity_temp)[i]]][["L3_Acc"]][["high_load_incorrect"]] <- ggplot(data = temp_plot_data,aes(x=XDFR_MRI_ACC_L3,y=`high load incorrect`))+
    geom_point()+
    stat_smooth(method="lm")+
    ylab("Similarity")+
    ggtitle("High Load - Incorrect trials")+
    theme_classic()
  
  delay_to_probe_plots[[names(similarity_temp)[i]]][["L3_Acc"]][["low_load_correct"]] <- ggplot(data = temp_plot_data,aes(x=XDFR_MRI_ACC_L3,y=`low load correct`))+
    geom_point()+
    stat_smooth(method="lm")+
    ylab("Similarity")+
    ggtitle("Low Load - Correct trials")+
    theme_classic()
  
  delay_to_probe_plots[[names(similarity_temp)[i]]][["L3_Acc"]][["high_load_correct"]] <- ggplot(data = temp_plot_data,aes(x=XDFR_MRI_ACC_L3,y=`high load correct`))+
    geom_point()+
    stat_smooth(method="lm")+
    ylab("Similarity")+
    ggtitle("High Load - Correct trials")+
    theme_classic()
  
  delay_to_probe_plots[[names(similarity_temp)[i]]][["BPRS"]][["low_load_incorrect"]] <- ggplot(data = temp_plot_data,aes(x=BPRS_TOT.x,y=`low load incorrect`))+
    geom_point()+
    stat_smooth(method="lm")+
    ylab("Similarity")+
    ggtitle("Low Load - Incorrect trials")+
    theme_classic()
  
  delay_to_probe_plots[[names(similarity_temp)[i]]][["BPRS"]][["high_load_incorrect"]] <- ggplot(data = temp_plot_data,aes(x=BPRS_TOT.x,y=`high load incorrect`))+
    geom_point()+
    stat_smooth(method="lm")+
    ylab("Similarity")+
    ggtitle("High Load - Incorrect trials")+
    theme_classic()
  
  delay_to_probe_plots[[names(similarity_temp)[i]]][["BPRS"]][["low_load_correct"]] <- ggplot(data = temp_plot_data,aes(x=BPRS_TOT.x,y=`low load correct`))+
    geom_point()+
    stat_smooth(method="lm")+
    ylab("Similarity")+
    ggtitle("Low Load - Correct trials")+
    theme_classic()
  
  delay_to_probe_plots[[names(similarity_temp)[i]]][["BPRS"]][["high_load_correct"]] <- ggplot(data = temp_plot_data,aes(x=BPRS_TOT.x,y=`high load correct`))+
    geom_point()+
    stat_smooth(method="lm")+
    ylab("Similarity")+
    ggtitle("High Load - Correct trials")+
    theme_classic()
}

temp_plot_data <- cbind.data.frame(data_to_plot,similarity_temp[["correct_delay_to_correct_probe"]])
colnames(temp_plot_data)[9] <- "correct_delay_probe"

delay_to_probe_plots[["correct_delay_to_correct_probe"]][["omnibus"]] <- ggplot(data = 
                                                                                        temp_plot_data,aes(x=omnibus_span_no_DFR_MRI,y=correct_delay_probe))+
  geom_point()+
  stat_smooth(method="lm")+
  ylab("Similarity")+
  ggtitle("Template delay/probe vs Omnibus Span")+
  theme_classic()

delay_to_probe_plots[["correct_delay_to_correct_probe"]][["BPRS"]] <- ggplot(data = 
                                                                                     temp_plot_data,aes(x=BPRS_TOT.x,y=correct_delay_probe))+
  geom_point()+
  stat_smooth(method="lm")+
  ylab("Similarity")+
  ggtitle("Template delay/probe vs BPRS")+
  theme_classic()

delay_to_probe_plots[["correct_delay_to_correct_probe"]][["L3_Acc"]] <- ggplot(data = 
                                                                                       temp_plot_data,aes(x=XDFR_MRI_ACC_L3,y=correct_delay_probe))+
  geom_point()+
  stat_smooth(method="lm")+
  ylab("Similarity")+
  ggtitle("Template delay/probe vs L3 accuracy")+
  theme_classic()

In these, graphs, if a correlation is black, it is below p < 0.05; if it is not, it is grey.

correlations = list()

for (i in c(3,8,9)){
  colnames(similarity_temp[[i]]) <- unlist(similarity_temp[[1]][[1]])
  temp_list <- list(r = data.frame(matrix(nrow=4,ncol=6)), p = data.frame(matrix(nrow=4,ncol=6)))
  
  for (behav in seq.int(2,7)){
    for (sim in seq.int(1,4)){ 
      temp_corr <- cor.test(similarity_temp[[i]][,sim],data_to_plot[,behav])
      temp_list[["r"]][sim,behav-1] <- temp_corr$estimate
      temp_list[["p"]][sim,behav-1] <- temp_corr$p.value
    }
  }
  
  colnames(temp_list[["r"]]) <- colnames(data_to_plot)[2:7]
  rownames(temp_list[["r"]]) <- colnames(similarity_temp[[i]])
  colnames(temp_list[["p"]]) <- colnames(data_to_plot)[2:7]
  rownames(temp_list[["p"]]) <- colnames(similarity_temp[[i]])
  
  
  correlations[[names(similarity_temp)[i]]] <- temp_list
  
}
temp <- data.frame(r=matrix(nrow=6,ncol=1),p=matrix(nrow=6,ncol=1))
rownames(temp) <- colnames(data_to_plot)[2:7]


for (behav in seq.int(2,7)){
  temp_corr <- cor.test(similarity_temp[["correct_delay_to_correct_probe"]],data_to_plot[,behav])
  temp$r[behav-1] <- temp_corr$estimate
  temp$p[behav-2] <- temp_corr$p.value
}

correlations[["correct_delay_to_correct_probe"]] <- temp

Individual delay to Individual Probe

Here, taking the correlation between multivariate representation at delay (TR 5) and probe (TR 11) on each trial.

Here, we’re seeing correlation between correct trials, regardless of load.

correlations[["delay_to_probe_avg"]][["r"]] %>% 
  mutate(
    condition = row.names(.),
    omnibus_span_no_DFR_MRI = cell_spec(omnibus_span_no_DFR_MRI, "html", 
                                        color =ifelse(correlations[["delay_to_probe_avg"]][["p"]]$omnibus_span_no_DFR_MRI < 0.05, "black", "grey")),
    XDFR_MRI_ACC_L3 = cell_spec(XDFR_MRI_ACC_L3, "html", 
                                color =ifelse(correlations[["delay_to_probe_avg"]][["p"]]$XDFR_MRI_ACC_L3 < 0.05, "black", "grey")),
    BPRS_TOT.x = cell_spec(BPRS_TOT.x, "html", 
                           color =ifelse(correlations[["delay_to_probe_avg"]][["p"]]$BPRS_TOT.x < 0.05, "black", "grey"))
  ) %>% 
  select(condition,omnibus_span_no_DFR_MRI,XDFR_MRI_ACC_L3,BPRS_TOT.x) %>%
  kable(format = "html", escape = F) %>%
  kable_styling("striped", full_width = F) %>% 
  add_header_above((c(" ", "Individual delay to Individual Probe" = 3)))

	Individual delay to Individual Probe
condition	omnibus_span_no_DFR_MRI	XDFR_MRI_ACC_L3	BPRS_TOT.x
low load incorrect	0.0999474832000594	0.101840689845703	-0.0214818240399329
high load incorrect	0.0285989286650679	-0.0437534440804446	-0.0699533108969534
low load correct	-0.0617493074180673	-0.171188164552925	-0.0661245734978317
high load correct	0.0243411825232576	-0.15875421838959	-0.0783033920592679

This measure can’t distinguish between either load or accuracy.

delay_to_probe_avg <- fisherz(similarity_temp[["delay_to_probe_avg"]])

t.test(delay_to_probe_avg[,4],delay_to_probe_avg[,2],paired=TRUE)

## 
##  Paired t-test
## 
## data:  delay_to_probe_avg[, 4] and delay_to_probe_avg[, 2]
## t = -0.65136, df = 169, p-value = 0.5157
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.02482484  0.01250702
## sample estimates:
## mean of the differences 
##            -0.006158913

t.test(delay_to_probe_avg[,4],delay_to_probe_avg[,3],paired=TRUE)

## 
##  Paired t-test
## 
## data:  delay_to_probe_avg[, 4] and delay_to_probe_avg[, 3]
## t = 0.70064, df = 169, p-value = 0.4845
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.008108084  0.017029941
## sample estimates:
## mean of the differences 
##             0.004460928

(delay_to_probe_plots[["delay_to_probe_avg"]][["omnibus"]][["high_load_correct"]] + delay_to_probe_plots[["delay_to_probe_avg"]][["omnibus"]][["high_load_incorrect"]]) /
  (delay_to_probe_plots[["delay_to_probe_avg"]][["omnibus"]][["low_load_correct"]] + 
     delay_to_probe_plots[["delay_to_probe_avg"]][["omnibus"]][["low_load_incorrect"]])+
  plot_annotation(title = "Individual trials vs omnibus")

## Warning: Removed 57 rows containing non-finite values (stat_smooth).

## Warning: Removed 57 rows containing missing values (geom_point).

(delay_to_probe_plots[["delay_to_probe_avg"]][["L3_Acc"]][["high_load_correct"]] + delay_to_probe_plots[["delay_to_probe_avg"]][["L3_Acc"]][["high_load_incorrect"]]) /
  (delay_to_probe_plots[["delay_to_probe_avg"]][["L3_Acc"]][["low_load_correct"]] + 
     delay_to_probe_plots[["delay_to_probe_avg"]][["L3_Acc"]][["low_load_incorrect"]])+
  plot_annotation(title = "Individual trials vs High Load Accuracy")

## Warning: Removed 57 rows containing non-finite values (stat_smooth).

## Warning: Removed 57 rows containing missing values (geom_point).

(delay_to_probe_plots[["delay_to_probe_avg"]][["BPRS"]][["high_load_correct"]] + delay_to_probe_plots[["delay_to_probe_avg"]][["BPRS"]][["high_load_incorrect"]]) /
  (delay_to_probe_plots[["delay_to_probe_avg"]][["BPRS"]][["low_load_correct"]] + 
     delay_to_probe_plots[["delay_to_probe_avg"]][["BPRS"]][["low_load_incorrect"]])+
  plot_annotation(title = "Individual trials vs BPRS Total")

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 58 rows containing non-finite values (stat_smooth).

## Warning: Removed 58 rows containing missing values (geom_point).

Individual Trial Delay vs Correct Template Probe

Same as before - only seeing a relationship between similarity and correct trials, regardless of load.

correlations[["delay_to_correct_probe_avg"]][["r"]] %>% 
  mutate(
    condition = row.names(.),
    omnibus_span_no_DFR_MRI = cell_spec(omnibus_span_no_DFR_MRI, "html", 
                                        color =ifelse(correlations[["delay_to_correct_probe_avg"]][["p"]]$omnibus_span_no_DFR_MRI < 0.05, "black", "grey")),
    XDFR_MRI_ACC_L3 = cell_spec(XDFR_MRI_ACC_L3, "html", 
                                color =ifelse(correlations[["delay_to_correct_probe_avg"]][["p"]]$XDFR_MRI_ACC_L3 < 0.05, "black", "grey")),
    BPRS_TOT.x = cell_spec(BPRS_TOT.x, "html", 
                           color =ifelse(correlations[["delay_to_correct_probe_avg"]][["p"]]$BPRS_TOT.x < 0.05, "black", "grey"))
  ) %>% 
  select(condition,omnibus_span_no_DFR_MRI,XDFR_MRI_ACC_L3,BPRS_TOT.x) %>%
  kable(format = "html", escape = F) %>%
  kable_styling("striped", full_width = F) %>% 
  add_header_above((c(" ", "Individual delay to Template probe" = 3)))

	Individual delay to Template probe
condition	omnibus_span_no_DFR_MRI	XDFR_MRI_ACC_L3	BPRS_TOT.x
low load incorrect	0.0177696165294611	-0.0764157965740026	-0.0647113955562519
high load incorrect	0.0239315782060482	-0.108487267294542	0.0626072686324235
low load correct	0.0576380942776104	-0.160013167275548	-0.0673209132769745
high load correct	0.0699602946380556	-0.231199129139215	0.0707735244701477

This measure distinguishes only between accuracy, but not load.

delay_to_correct_probe_avg <- fisherz(similarity_temp[["delay_to_correct_probe_avg"]])

t.test(delay_to_correct_probe_avg[,4],delay_to_correct_probe_avg[,2],paired=TRUE)

## 
##  Paired t-test
## 
## data:  delay_to_correct_probe_avg[, 4] and delay_to_correct_probe_avg[, 2]
## t = -2.7392, df = 169, p-value = 0.006819
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.03716834 -0.00603378
## sample estimates:
## mean of the differences 
##             -0.02160106

t.test(delay_to_correct_probe_avg[,4],delay_to_correct_probe_avg[,3],paired=TRUE)

## 
##  Paired t-test
## 
## data:  delay_to_correct_probe_avg[, 4] and delay_to_correct_probe_avg[, 3]
## t = 0.99115, df = 169, p-value = 0.323
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.005798725  0.017492879
## sample estimates:
## mean of the differences 
##             0.005847077

(delay_to_probe_plots[["delay_to_correct_probe_avg"]][["omnibus"]][["high_load_correct"]] + delay_to_probe_plots[["delay_to_correct_probe_avg"]][["omnibus"]][["high_load_incorrect"]]) /
  (delay_to_probe_plots[["delay_to_correct_probe_avg"]][["omnibus"]][["low_load_correct"]] + 
     delay_to_probe_plots[["delay_to_correct_probe_avg"]][["omnibus"]][["low_load_incorrect"]])+
  plot_annotation(title = "Individual trials vs omnibus")

## Warning: Removed 57 rows containing non-finite values (stat_smooth).

## Warning: Removed 57 rows containing missing values (geom_point).

(delay_to_probe_plots[["delay_to_correct_probe_avg"]][["L3_Acc"]][["high_load_correct"]] + delay_to_probe_plots[["delay_to_correct_probe_avg"]][["L3_Acc"]][["high_load_incorrect"]]) /
  (delay_to_probe_plots[["delay_to_correct_probe_avg"]][["L3_Acc"]][["low_load_correct"]] + 
     delay_to_probe_plots[["delay_to_correct_probe_avg"]][["L3_Acc"]][["low_load_incorrect"]])+
  plot_annotation(title = "Individual trials vs High Load Accuracy")

## Warning: Removed 57 rows containing non-finite values (stat_smooth).

## Warning: Removed 57 rows containing missing values (geom_point).

(delay_to_probe_plots[["delay_to_correct_probe_avg"]][["BPRS"]][["high_load_correct"]] + delay_to_probe_plots[["delay_to_correct_probe_avg"]][["BPRS"]][["high_load_incorrect"]]) /
  (delay_to_probe_plots[["delay_to_correct_probe_avg"]][["BPRS"]][["low_load_correct"]] + 
     delay_to_probe_plots[["delay_to_correct_probe_avg"]][["BPRS"]][["low_load_incorrect"]])+
  plot_annotation(title = "Individual trials vs BPRS Total")

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 58 rows containing non-finite values (stat_smooth).

## Warning: Removed 58 rows containing missing values (geom_point).

Correct Template delay vs Individual Trial probe

Same as before - only relationship with correct trials.

correlations[["correct_delay_to_probe_avg"]][["r"]] %>% 
  mutate(
    condition = row.names(.),
    omnibus_span_no_DFR_MRI = cell_spec(omnibus_span_no_DFR_MRI, "html", 
                                        color =ifelse(correlations[["correct_delay_to_probe_avg"]][["p"]]$omnibus_span_no_DFR_MRI < 0.05, "black", "grey")),
    XDFR_MRI_ACC_L3 = cell_spec(XDFR_MRI_ACC_L3, "html", 
                                color =ifelse(correlations[["correct_delay_to_probe_avg"]][["p"]]$XDFR_MRI_ACC_L3 < 0.05, "black", "grey")),
    BPRS_TOT.x = cell_spec(BPRS_TOT.x, "html", 
                           color =ifelse(correlations[["correct_delay_to_probe_avg"]][["p"]]$BPRS_TOT.x < 0.05, "black", "grey"))
  ) %>% 
  select(condition,omnibus_span_no_DFR_MRI,XDFR_MRI_ACC_L3,BPRS_TOT.x) %>%
  kable(format = "html", escape = F) %>%
  kable_styling("striped", full_width = F) %>% 
  add_header_above((c(" ", "Template delay to probe" = 3)))

	Template delay to probe
condition	omnibus_span_no_DFR_MRI	XDFR_MRI_ACC_L3	BPRS_TOT.x
low load incorrect	0.0372099118469689	0.00968201164217853	0.111065967359362
high load incorrect	0.0267511870180811	-0.0382426602626256	0.0464791406312869
low load correct	-0.0097945052491883	-0.165867737691321	0.0533419833418061
high load correct	0.00768243154725069	-0.15887892851924	0.0455135323513504

This measure only distinguishes accuracy.

correct_delay_to_probe_avg <- fisherz(similarity_temp[["correct_delay_to_probe_avg"]])

t.test(correct_delay_to_probe_avg[,4],correct_delay_to_probe_avg[,2],paired=TRUE)

## 
##  Paired t-test
## 
## data:  correct_delay_to_probe_avg[, 4] and correct_delay_to_probe_avg[, 2]
## t = -2.1722, df = 169, p-value = 0.03123
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.033911450 -0.001620536
## sample estimates:
## mean of the differences 
##             -0.01776599

t.test(correct_delay_to_probe_avg[,4],correct_delay_to_probe_avg[,3],paired=TRUE)

## 
##  Paired t-test
## 
## data:  correct_delay_to_probe_avg[, 4] and correct_delay_to_probe_avg[, 3]
## t = -1.4859, df = 169, p-value = 0.1392
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.020746555  0.002927505
## sample estimates:
## mean of the differences 
##            -0.008909525

(delay_to_probe_plots[["correct_delay_to_probe_avg"]][["omnibus"]][["high_load_correct"]] + delay_to_probe_plots[["correct_delay_to_probe_avg"]][["omnibus"]][["high_load_incorrect"]]) /
  (delay_to_probe_plots[["correct_delay_to_probe_avg"]][["omnibus"]][["low_load_correct"]] + 
     delay_to_probe_plots[["correct_delay_to_probe_avg"]][["omnibus"]][["low_load_incorrect"]])+
  plot_annotation(title = "Individual trials vs omnibus")

## Warning: Removed 57 rows containing non-finite values (stat_smooth).

## Warning: Removed 57 rows containing missing values (geom_point).

(delay_to_probe_plots[["correct_delay_to_probe_avg"]][["L3_Acc"]][["high_load_correct"]] + delay_to_probe_plots[["correct_delay_to_probe_avg"]][["L3_Acc"]][["high_load_incorrect"]]) /
  (delay_to_probe_plots[["correct_delay_to_probe_avg"]][["L3_Acc"]][["low_load_correct"]] + 
     delay_to_probe_plots[["correct_delay_to_probe_avg"]][["L3_Acc"]][["low_load_incorrect"]])+
  plot_annotation(title = "Individual trials vs High Load Accuracy")

## Warning: Removed 57 rows containing non-finite values (stat_smooth).

## Warning: Removed 57 rows containing missing values (geom_point).

(delay_to_probe_plots[["correct_delay_to_probe_avg"]][["BPRS"]][["high_load_correct"]] + delay_to_probe_plots[["correct_delay_to_probe_avg"]][["BPRS"]][["high_load_incorrect"]]) /
  (delay_to_probe_plots[["correct_delay_to_probe_avg"]][["BPRS"]][["low_load_correct"]] + 
     delay_to_probe_plots[["correct_delay_to_probe_avg"]][["BPRS"]][["low_load_incorrect"]])+
  plot_annotation(title = "Individual trials vs BPRS Total")

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 58 rows containing non-finite values (stat_smooth).

## Warning: Removed 58 rows containing missing values (geom_point).

Correct template delay to correct template probe

Again, we’re only seeing relationships between the correlation and with accuracy.

correlations[["correct_delay_to_correct_probe"]][c(2,3,5),] %>% 
  mutate(
    condition = row.names(.),
    r = cell_spec(r, "html", 
                  color =ifelse(p < 0.05, "black", "grey"))
  )  %>% 
  select(condition,r) %>%
  kable(format = "html", escape = F) %>%
  kable_styling("striped", full_width = F) %>%
  add_header_above(c(" ", "Template delay to Template Probe" = 1))

	Template delay to Template Probe
condition	r
omnibus_span_no_DFR_MRI	0.0573977265501652
XDFR_MRI_ACC_L3	-0.12504600857427
BPRS_TOT.x	-0.0544283394074548

delay_to_probe_plots[["correct_delay_to_correct_probe"]][["omnibus"]]

delay_to_probe_plots[["correct_delay_to_correct_probe"]][["L3_Acc"]]

delay_to_probe_plots[["correct_delay_to_correct_probe"]][["BPRS"]]

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

DFR

Now, we move on to regions associated with load effects.

similarity_temp <- read.mat('data/intertrial_similarity_DFR.mat')

for (i in seq.int(14,17)){
  similarity_temp[[i]] <- data.frame(similarity_temp[[i]])
  similarity_temp[[i]][similarity_temp[[i]]==0] <- NA
  similarity_temp[[i]]$PTID <- constructs_fMRI$PTID
}

cond_avgs <- data.frame(matrix(nrow=4,ncol=14))

cond_avgs[1,] <- colMeans(similarity_temp[[14]][,1:14],na.rm=TRUE)
cond_avgs[2,] <- colMeans(similarity_temp[[15]][,1:14],na.rm=TRUE)
cond_avgs[3,] <- colMeans(similarity_temp[[16]][,1:14],na.rm=TRUE)
cond_avgs[4,] <- colMeans(similarity_temp[[17]][,1:14],na.rm=TRUE)

cond_avgs$group <- factor(names(similarity_temp)[14:17])
colnames(cond_avgs)[1:14] <- c(1:14)

se_avgs <- data.frame(matrix(nrow=4,ncol=14))
se_avgs[1,] <- sapply(similarity_temp[[14]][,1:14],se)
se_avgs[2,] <- sapply(similarity_temp[[15]][,1:14],se)
se_avgs[3,] <- sapply(similarity_temp[[16]][,1:14],se)
se_avgs[4,] <- sapply(similarity_temp[[17]][,1:14],se)
se_avgs$group <- factor(names(similarity_temp)[14:17])
colnames(se_avgs)[1:14] <- c(1:14)

cond_melt <- melt(cond_avgs,id_vars=c("group"))

## Using group as id variables

colnames(cond_melt) <- c("group", "TR", "similarity")
cond_melt$TR <- as.numeric(as.character(cond_melt$TR))

se_melt <- melt(se_avgs,id.vars="group")
colnames(se_melt) <- c("group", "TR", "se")
se_melt$TR <- as.numeric(as.character(se_melt$TR))

melt_avg_data <- merge(cond_melt,se_melt,by=c("group","TR"))
melt_avg_data$se_min <- melt_avg_data$similarity-melt_avg_data$se
melt_avg_data$se_max <- melt_avg_data$similarity+melt_avg_data$se

Average Similarity

Although slightly larger errors and slightly lower correlations, seeing the same patterns as in the fusiform.

For this mask, we see the differences in accuracy at high load at TRs 5, 6, 8, 11, 13 and 14 and at TRs 1, 2, 4-11, 13 and 14 for low load. We can distinguish between load for correct trials at TRs 2-7, 11, 12, and for incorrect trials at TRs 1-2, 3-7, 10-14.

ggplot(data=melt_avg_data,aes(x=TR,y=similarity))+
  geom_line(aes(color=group)) +
  geom_ribbon(aes(ymin=se_min,ymax=se_max,fill=group),alpha=0.2)+
  scale_x_continuous(breaks = c(1:14),labels=c(1:14))+
  ggtitle("Intertrial similarity averaged over all subjects")+
  theme_classic()

corrected_p_val <- 0.05/14

low_load_acc_test <- data.frame(matrix(nrow=3,ncol=14))
colnames(low_load_acc_test) <- paste("TR_",c(1:14))
rownames(low_load_acc_test) <- c("t","p","corrected_p")

high_load_acc_test <- data.frame(matrix(nrow=3,ncol=14))
colnames(high_load_acc_test) <- paste("TR_",c(1:14))
rownames(high_load_acc_test) <- c("t","p","corrected_sig")

for (time in seq.int(1,14)){
  low_test <- t.test(similarity_temp[[16]][,time],similarity_temp[[17]][,time],paired=TRUE)
  high_test <- t.test(similarity_temp[[14]][,time],similarity_temp[[15]][,time],paired=TRUE)
  
  low_load_acc_test[1,time] <- low_test$statistic
  low_load_acc_test[2,time] <- low_test$p.value
  if (low_test$p.value < corrected_p_val){low_load_acc_test[3,time] <- 1}else{low_load_acc_test[3,time] <- 0}
  
  high_load_acc_test[1,time] <- high_test$statistic
  high_load_acc_test[2,time] <- high_test$p.value
  if (high_test$p.value < corrected_p_val){high_load_acc_test[3,time] <- 1}else{high_load_acc_test[3,time] <- 0}
  
}

high_load_acc_test %>% 
  kable(format = "html", escape = F) %>%
  kable_styling("striped", full_width = F) %>% 
  add_header_above((c(" ", "t-test between correct and incorrect values at each time point, high load trials" = 14)))

	t-test between correct and incorrect values at each time point, high load trials
	TR_ 1	TR_ 2	TR_ 3	TR_ 4	TR_ 5	TR_ 6	TR_ 7	TR_ 8	TR_ 9	TR_ 10	TR_ 11	TR_ 12	TR_ 13	TR_ 14
t	2.8892262	2.7912080	2.0493672	2.6898779	4.5071758	3.8798962	2.8603023	3.0914476	2.4548455	2.5887679	3.3097106	2.7078641	3.0584788	3.9483085
p	0.0043682	0.0058557	0.0419719	0.0078649	0.0000122	0.0001495	0.0047665	0.0023303	0.0151077	0.0104713	0.0011416	0.0074681	0.0025873	0.0001153
corrected_sig	0.0000000	0.0000000	0.0000000	0.0000000	1.0000000	1.0000000	0.0000000	1.0000000	0.0000000	0.0000000	1.0000000	0.0000000	1.0000000	1.0000000

low_load_acc_test %>% 
  kable(format = "html", escape = F) %>%
  kable_styling("striped", full_width = F) %>% 
  add_header_above((c(" ", "t-test between correct and incorrect values at each time point, low load trials" = 14)))

	t-test between correct and incorrect values at each time point, low load trials
	TR_ 1	TR_ 2	TR_ 3	TR_ 4	TR_ 5	TR_ 6	TR_ 7	TR_ 8	TR_ 9	TR_ 10	TR_ 11	TR_ 12	TR_ 13	TR_ 14
t	4.5677474	3.9554849	2.4179737	3.5077300	4.2436080	3.9153612	3.6523970	4.351031	5.0480337	5.7048552	4.3024208	2.2214536	4.5065711	6.026869
p	0.0000127	0.0001343	0.0172198	0.0006516	0.0000455	0.0001555	0.0003967	0.000030	0.0000017	0.0000001	0.0000363	0.0283313	0.0000163	0.000000
corrected_p	1.0000000	1.0000000	0.0000000	1.0000000	1.0000000	1.0000000	1.0000000	1.000000	1.0000000	1.0000000	1.0000000	0.0000000	1.0000000	1.000000

correct_load_test <- data.frame(matrix(nrow=3,ncol=14))
colnames(correct_load_test) <- paste("TR_",c(1:14))
rownames(correct_load_test) <- c("t","p","corrected_p")

inccorrect_load_test <- data.frame(matrix(nrow=3,ncol=14))
colnames(inccorrect_load_test) <- paste("TR_",c(1:14))
rownames(inccorrect_load_test) <- c("t","p","corrected_p")

for (time in seq.int(1,14)){
  correct_test <- t.test(similarity_temp[[14]][,time],similarity_temp[[16]][,time],paired=TRUE)
  incorrect_test <- t.test(similarity_temp[[15]][,time],similarity_temp[[17]][,time],paired=TRUE)
  
  correct_load_test[1,time] <- correct_test$statistic
  correct_load_test[2,time] <- correct_test$p.value
  if (correct_test$p.value < corrected_p_val){correct_load_test[3,time] <- 1}else{correct_load_test[3,time] <- 0}
  
  inccorrect_load_test[1,time] <- incorrect_test$statistic
  inccorrect_load_test[2,time] <- incorrect_test$p.value
  if (incorrect_test$p.value < corrected_p_val){inccorrect_load_test[3,time] <- 1}else{inccorrect_load_test[3,time] <- 0}
  
}

correct_load_test %>% 
  kable(format = "html", escape = F) %>%
  kable_styling("striped", full_width = F) %>% 
  add_header_above((c(" ", "t-test between high and low loads at each time point, correct trials" = 14)))

	t-test between high and low loads at each time point, correct trials
	TR_ 1	TR_ 2	TR_ 3	TR_ 4	TR_ 5	TR_ 6	TR_ 7	TR_ 8	TR_ 9	TR_ 10	TR_ 11	TR_ 12	TR_ 13	TR_ 14
t	0.6180727	3.0064805	4.0083435	5.3460520	10.45517	10.62552	5.2862178	0.2916840	-1.7133688	2.2720350	9.108687	8.469332	2.5713662	2.103212
p	0.5373595	0.0030462	0.0000916	0.0000003	0.00000	0.00000	0.0000004	0.7708859	0.0884784	0.0243449	0.000000	0.000000	0.0109911	0.036928
corrected_p	0.0000000	1.0000000	1.0000000	1.0000000	1.00000	1.00000	1.0000000	0.0000000	0.0000000	0.0000000	1.000000	1.000000	0.0000000	0.000000

inccorrect_load_test %>% 
  kable(format = "html", escape = F) %>%
  kable_styling("striped", full_width = F) %>% 
  add_header_above((c(" ", "t-test between high and low loads at each time point, incorrect trials" = 14)))

	t-test between high and low loads at each time point, incorrect trials
	TR_ 1	TR_ 2	TR_ 3	TR_ 4	TR_ 5	TR_ 6	TR_ 7	TR_ 8	TR_ 9	TR_ 10	TR_ 11	TR_ 12	TR_ 13	TR_ 14
t	2.987347	3.1658370	2.7808537	5.0031235	7.259286	8.209648	5.0380589	2.284257	2.5202473	5.5333814	6.030818	4.1353410	3.6298704	4.1161827
p	0.003458	0.0019922	0.0063622	0.0000021	0.000000	0.000000	0.0000018	0.024241	0.0131361	0.0000002	0.000000	0.0000688	0.0004289	0.0000739
corrected_p	1.000000	1.0000000	0.0000000	1.0000000	1.000000	1.000000	1.0000000	0.000000	0.0000000	1.0000000	1.000000	1.0000000	1.0000000	1.0000000

split_similarity <- list()
split_sim_avgs <- list()


for (i in seq.int(14,17)){ 
  split_similarity[[names(similarity_temp)[i]]] <- split_into_groups(similarity_temp[[i]],WM_groups)
  colnames(split_similarity[[i-13]][["all"]])[1:14] <- c(1:14)
  
  for (level in seq.int(1,3)){
    temp_data <- data.frame(mean=colMeans(split_similarity[[i-13]][[level]][1:14],na.rm=TRUE),se = sapply(split_similarity[[i-13]][[level]][1:14],se),
                            se_min = colMeans(split_similarity[[i-13]][[level]][1:14],na.rm=TRUE) - sapply(split_similarity[[i-13]][[level]][1:14],se),
                            se_max = colMeans(split_similarity[[i-13]][[level]][1:14],na.rm=TRUE) + sapply(split_similarity[[i-13]][[level]][1:14],se))
    split_sim_avgs[[names(split_similarity)[i-13]]][[names(split_similarity[[i-13]])[level]]] <- data.frame((temp_data))
    split_sim_avgs[[i-13]][[level]]$group <- rep(names(split_similarity[[i-13]])[level],14)
    split_sim_avgs[[i-13]][[level]]$TR <- seq.int(1,14)
    
  }
  
  split_sim_avgs[[i-13]][["all"]] <- rbind(split_sim_avgs[[i-13]][["high"]],split_sim_avgs[[i-13]][["med"]],split_sim_avgs[[i-13]][["low"]])
  
  split_sim_avgs[[i-13]][["all"]]$group <- factor(split_sim_avgs[[i-13]][["all"]]$group, levels=c("high","med","low"))
  
}

Split into groups

Same story as in fusiform, except we also a little more group distinction in the encoding period in the high load correct trials, where high and medium capacity subjects show increased similarity vs low capacity subjects.

sim_plots <- list()

for (i in seq.int(1,4)){
  sim_plots[[i]] <- ggplot(data = split_sim_avgs[[i]][["all"]])+
    geom_line(aes(x=TR,y=mean,color=group))+
    geom_ribbon(aes(x=TR,ymin=se_min,ymax=se_max,fill=group),alpha=0.2)+
    scale_x_continuous(breaks = c(1:14),labels=c(1:14))+
    ggtitle(names(split_sim_avgs)[i])+
    ylab("Mean Similarity")+
    theme_classic()
  
}

(sim_plots[[1]] + sim_plots[[2]]) / (sim_plots[[3]] + sim_plots[[4]])+
  plot_layout(guides = "collect")+
  plot_annotation(title="Inter-trial Similarity")

data_to_plot <- merge(constructs_fMRI,p200_data,by="PTID")
data_to_plot <- merge(data_to_plot,p200_clinical_zscores,by="PTID")

data_to_plot <- data_to_plot[,c(1,6,7,13,14,40,41)]
data_to_plot$ACC_LE <- data_to_plot$XDFR_MRI_ACC_L3 - data_to_plot$XDFR_MRI_ACC_L1

corr_to_behav_plots <- list()

for (i in seq.int(14,17)){
  measure_by_time <- data.frame(matrix(nrow=4,ncol=14))
  
  for (measure in seq.int(3,6)){
    for (TR in seq.int(1,14)){
      measure_by_time[measure-2,TR] <- cor(data_to_plot[,measure],similarity_temp[[i]][,TR],use="pairwise.complete.obs")
    }
  }
  
  measure_by_time <- data.frame(t(measure_by_time))
  measure_by_time$TR <- seq.int(1,14)
  
  colnames(measure_by_time)[1:4] <- colnames(data_to_plot)[3:6]
  
  melted_measure_by_time <- melt(measure_by_time,id.vars="TR")
  
  corr_to_behav_plots[[names(similarity_temp)[i]]] <- ggplot(data=melted_measure_by_time,aes(x=TR,y=value))+
    geom_line(aes(color=variable))+
    scale_x_continuous(breaks = c(1:14),labels=c(1:14))+
    ggtitle(names(similarity_temp)[i])+
    theme_classic()
  
}

Compare to behavioral measures

Over time

Here, we’re actually seeing something a little different and interesting - stronger correlations overall, and omnibus span is most related during the encoding and probe periods, but not delay periods. There also isn’t any correlation with the clinical measures.

(corr_to_behav_plots[[1]] + corr_to_behav_plots[[2]]) / (corr_to_behav_plots[[3]] + corr_to_behav_plots[[4]])+
  plot_layout(guides="collect")+
  plot_annotation(title = "Correlation between inter-trial similarity and behavioral measure")

scatter_plots_delay <- list()
scatter_plots_cue <- list()
scatter_plots_probe <- list()

for (i in seq.int(14,17)){
  temp_plot_data <- merge(data_to_plot,similarity_temp[[i]],by="PTID")
  scatter_plots_delay[[names(similarity_temp)[i]]][["omnibus"]]  <- ggplot(data=temp_plot_data)+
    geom_point(aes(x=omnibus_span_no_DFR_MRI,y=X8))+
    stat_smooth(aes(x=omnibus_span_no_DFR_MRI,y=X8),method="lm")+
    scale_x_continuous(breaks = c(1:14),labels=c(1:14))+
    ggtitle(names(similarity_temp)[i])+
    ylab("Inter-trial similarity")+
    theme_classic()
  
  scatter_plots_delay[[names(similarity_temp)[i]]][["BPRS"]]  <- ggplot(data=temp_plot_data)+
    geom_point(aes(x=BPRS_TOT.x,y=X8))+
    stat_smooth(aes(x=BPRS_TOT.x,y=X8),method="lm")+
    scale_x_continuous(breaks = c(1:14),labels=c(1:14))+
    ggtitle(names(similarity_temp)[i])+
    ylab("Inter-trial similarity")+
    theme_classic()
  
  scatter_plots_delay[[names(similarity_temp)[i]]][["L3_acc"]]  <- ggplot(data=temp_plot_data)+
    geom_point(aes(x=XDFR_MRI_ACC_L3,y=X8))+
    stat_smooth(aes(x=XDFR_MRI_ACC_L3,y=X8),method="lm")+
    scale_x_continuous(breaks = c(1:14),labels=c(1:14))+
    ggtitle(names(similarity_temp)[i])+
    ylab("Inter-trial similarity")+
    theme_classic()
  
  scatter_plots_cue[[names(similarity_temp)[i]]][["omnibus"]]  <- ggplot(data=temp_plot_data)+
    geom_point(aes(x=omnibus_span_no_DFR_MRI,y=X6))+
    stat_smooth(aes(x=omnibus_span_no_DFR_MRI,y=X6),method="lm")+
    scale_x_continuous(breaks = c(1:14),labels=c(1:14))+
    ggtitle(names(similarity_temp)[i])+
    ylab("Inter-trial similarity")+
    theme_classic()
  
  scatter_plots_cue[[names(similarity_temp)[i]]][["BPRS"]]  <- ggplot(data=temp_plot_data)+
    geom_point(aes(x=BPRS_TOT.x,y=X6))+
    stat_smooth(aes(x=BPRS_TOT.x,y=X6),method="lm")+
    scale_x_continuous(breaks = c(1:14),labels=c(1:14))+
    ggtitle(names(similarity_temp)[i])+
    ylab("Inter-trial similarity")+
    theme_classic()
  
  scatter_plots_cue[[names(similarity_temp)[i]]][["L3_acc"]]  <- ggplot(data=temp_plot_data)+
    geom_point(aes(x=XDFR_MRI_ACC_L3,y=X6))+
    stat_smooth(aes(x=XDFR_MRI_ACC_L3,y=X6),method="lm")+
    scale_x_continuous(breaks = c(1:14),labels=c(1:14))+
    ggtitle(names(similarity_temp)[i])+
    ylab("Inter-trial similarity")+
    theme_classic()
  
    scatter_plots_probe[[names(similarity_temp)[i]]][["omnibus"]]  <- ggplot(data=temp_plot_data)+
    geom_point(aes(x=omnibus_span_no_DFR_MRI,y=X11))+
    stat_smooth(aes(x=omnibus_span_no_DFR_MRI,y=X11),method="lm")+
    scale_x_continuous(breaks = c(1:14),labels=c(1:14))+
    ggtitle(names(similarity_temp)[i])+
    ylab("Inter-trial similarity")+
    theme_classic()
  
  scatter_plots_probe[[names(similarity_temp)[i]]][["BPRS"]]  <- ggplot(data=temp_plot_data)+
    geom_point(aes(x=BPRS_TOT.x,y=X11))+
    stat_smooth(aes(x=BPRS_TOT.x,y=X11),method="lm")+
    scale_x_continuous(breaks = c(1:14),labels=c(1:14))+
    ggtitle(names(similarity_temp)[i])+
    ylab("Inter-trial similarity")+
    theme_classic()
  
  scatter_plots_probe[[names(similarity_temp)[i]]][["L3_acc"]]  <- ggplot(data=temp_plot_data)+
    geom_point(aes(x=XDFR_MRI_ACC_L3,y=X11))+
    stat_smooth(aes(x=XDFR_MRI_ACC_L3,y=X11),method="lm")+
    scale_x_continuous(breaks = c(1:14),labels=c(1:14))+
    ggtitle(names(similarity_temp)[i])+
    ylab("Inter-trial similarity")+
    theme_classic()
}

Encoding Period

Just like before, not seeing any non-linear relationships, though the relationship between accuracy and similarity is statistically significant for all conditions except for low load incorrect trials.

(scatter_plots_cue[[1]][["omnibus"]] + scatter_plots_cue[[2]][["omnibus"]]) /
  (scatter_plots_cue[[3]][["omnibus"]] + scatter_plots_cue[[4]][["omnibus"]])+
  plot_layout(guides="collect")+
  plot_annotation(title = "Omnibus span vs inter-trial similiarity - encoding")

## Warning: Removed 57 rows containing non-finite values (stat_smooth).

## Warning: Removed 57 rows containing missing values (geom_point).

cor.test(similarity_temp[["high_correct_avg"]]$X5,data_to_plot$omnibus_span_no_DFR_MRI)

## 
##  Pearson's product-moment correlation
## 
## data:  similarity_temp[["high_correct_avg"]]$X5 and data_to_plot$omnibus_span_no_DFR_MRI
## t = 1.5617, df = 168, p-value = 0.1202
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.03146036  0.26535694
## sample estimates:
##       cor 
## 0.1196203

cor.test(similarity_temp[["high_incorrect_avg"]]$X5,data_to_plot$omnibus_span_no_DFR_MRI)

## 
##  Pearson's product-moment correlation
## 
## data:  similarity_temp[["high_incorrect_avg"]]$X5 and data_to_plot$omnibus_span_no_DFR_MRI
## t = 1.2925, df = 168, p-value = 0.198
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.05206472  0.24606635
## sample estimates:
##        cor 
## 0.09922712

(scatter_plots_cue[[1]][["BPRS"]] + scatter_plots_cue[[2]][["BPRS"]]) /
  (scatter_plots_cue[[3]][["BPRS"]] + scatter_plots_cue[[4]][["BPRS"]])+
  plot_layout(guides="collect")+
  plot_annotation(title = "BPRS span vs inter-trial similiarity - encoding")

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 58 rows containing non-finite values (stat_smooth).

## Warning: Removed 58 rows containing missing values (geom_point).

cor.test(similarity_temp[["high_correct_avg"]]$X5,data_to_plot$BPRS_TOT.x)

## 
##  Pearson's product-moment correlation
## 
## data:  similarity_temp[["high_correct_avg"]]$X5 and data_to_plot$BPRS_TOT.x
## t = -0.4146, df = 167, p-value = 0.679
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.1821446  0.1194721
## sample estimates:
##         cor 
## -0.03206624

cor.test(similarity_temp[["high_incorrect_avg"]]$X5,data_to_plot$BPRS_TOT.x)

## 
##  Pearson's product-moment correlation
## 
## data:  similarity_temp[["high_incorrect_avg"]]$X5 and data_to_plot$BPRS_TOT.x
## t = 0.45649, df = 167, p-value = 0.6486
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.1162772  0.1852752
## sample estimates:
##        cor 
## 0.03530251

cor.test(similarity_temp[["low_incorrect_avg"]]$X5,data_to_plot$BPRS_TOT.x)

## 
##  Pearson's product-moment correlation
## 
## data:  similarity_temp[["low_incorrect_avg"]]$X5 and data_to_plot$BPRS_TOT.x
## t = -0.47578, df = 110, p-value = 0.6352
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.2289480  0.1414276
## sample estimates:
##         cor 
## -0.04531738

(scatter_plots_cue[[1]][["L3_acc"]] + scatter_plots_cue[[2]][["L3_acc"]]) /
  (scatter_plots_cue[[3]][["L3_acc"]] + scatter_plots_cue[[4]][["L3_acc"]])+
  plot_layout(guides="collect")+
  plot_annotation(title = "L3_acc span vs inter-trial similiarity - encoding")

## Warning: Removed 57 rows containing non-finite values (stat_smooth).

## Warning: Removed 57 rows containing missing values (geom_point).

cor.test(similarity_temp[["high_correct_avg"]]$X5,data_to_plot$XDFR_MRI_ACC_L3)

## 
##  Pearson's product-moment correlation
## 
## data:  similarity_temp[["high_correct_avg"]]$X5 and data_to_plot$XDFR_MRI_ACC_L3
## t = 4.5927, df = 168, p-value = 8.545e-06
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1931873 0.4613139
## sample estimates:
##     cor 
## 0.33399

cor.test(similarity_temp[["high_incorrect_avg"]]$X5,data_to_plot$XDFR_MRI_ACC_L3)

## 
##  Pearson's product-moment correlation
## 
## data:  similarity_temp[["high_incorrect_avg"]]$X5 and data_to_plot$XDFR_MRI_ACC_L3
## t = 2.7355, df = 168, p-value = 0.006897
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.05778316 0.34625363
## sample estimates:
##       cor 
## 0.2065014

cor.test(similarity_temp[["low_correct_avg"]]$X5,data_to_plot$XDFR_MRI_ACC_L3)

## 
##  Pearson's product-moment correlation
## 
## data:  similarity_temp[["low_correct_avg"]]$X5 and data_to_plot$XDFR_MRI_ACC_L3
## t = 2.2893, df = 168, p-value = 0.02331
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.02404603 0.31616805
## sample estimates:
##       cor 
## 0.1739308

cor.test(similarity_temp[["low_incorrect_avg"]]$X5,data_to_plot$XDFR_MRI_ACC_L3)

## 
##  Pearson's product-moment correlation
## 
## data:  similarity_temp[["low_incorrect_avg"]]$X5 and data_to_plot$XDFR_MRI_ACC_L3
## t = 0.57367, df = 111, p-value = 0.5674
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.1316830  0.2367217
## sample estimates:
##        cor 
## 0.05436942

Delay Period

Same - no non-linear relationships. Here, only the relationship with high load accuracy is related for the high load correct trials and low load incorrect trials.

(scatter_plots_delay[[1]][["omnibus"]] + scatter_plots_delay[[2]][["omnibus"]]) /
  (scatter_plots_delay[[3]][["omnibus"]] + scatter_plots_delay[[4]][["omnibus"]])+
  plot_layout(guides="collect")+
  plot_annotation(title = "Omnibus span vs inter-trial similiarity - delay")

## Warning: Removed 57 rows containing non-finite values (stat_smooth).

## Warning: Removed 57 rows containing missing values (geom_point).

cor.test(similarity_temp[["low_correct_avg"]]$X8,data_to_plot$omnibus_span_no_DFR_MRI)

## 
##  Pearson's product-moment correlation
## 
## data:  similarity_temp[["low_correct_avg"]]$X8 and data_to_plot$omnibus_span_no_DFR_MRI
## t = 1.1661, df = 168, p-value = 0.2452
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.06174178  0.23692407
## sample estimates:
##        cor 
## 0.08960518

(scatter_plots_delay[[1]][["BPRS"]] + scatter_plots_delay[[2]][["BPRS"]]) /
  (scatter_plots_delay[[3]][["BPRS"]] + scatter_plots_delay[[4]][["BPRS"]])+
  plot_layout(guides="collect")+
  plot_annotation(title = "BPRS vs inter-trial similiarity - delay")

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 58 rows containing non-finite values (stat_smooth).

## Warning: Removed 58 rows containing missing values (geom_point).

(scatter_plots_delay[[1]][["L3_acc"]] + scatter_plots_delay[[2]][["L3_acc"]]) /
  (scatter_plots_delay[[3]][["L3_acc"]] + scatter_plots_delay[[4]][["L3_acc"]])+
  plot_layout(guides="collect")+
  plot_annotation(title = "L3_acc vs inter-trial similiarity - delay")

## Warning: Removed 57 rows containing non-finite values (stat_smooth).

## Warning: Removed 57 rows containing missing values (geom_point).

cor.test(similarity_temp[["high_correct_avg"]]$X8,data_to_plot$XDFR_MRI_ACC_L3)

## 
##  Pearson's product-moment correlation
## 
## data:  similarity_temp[["high_correct_avg"]]$X8 and data_to_plot$XDFR_MRI_ACC_L3
## t = 3.4373, df = 168, p-value = 0.0007407
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1100649 0.3917331
## sample estimates:
##       cor 
## 0.2563326

cor.test(similarity_temp[["high_incorrect_avg"]]$X8,data_to_plot$XDFR_MRI_ACC_L3)

## 
##  Pearson's product-moment correlation
## 
## data:  similarity_temp[["high_incorrect_avg"]]$X8 and data_to_plot$XDFR_MRI_ACC_L3
## t = 1.7106, df = 168, p-value = 0.089
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.02006702  0.27592275
## sample estimates:
##       cor 
## 0.1308424

cor.test(similarity_temp[["low_correct_avg"]]$X8,data_to_plot$XDFR_MRI_ACC_L3)

## 
##  Pearson's product-moment correlation
## 
## data:  similarity_temp[["low_correct_avg"]]$X8 and data_to_plot$XDFR_MRI_ACC_L3
## t = 1.771, df = 168, p-value = 0.07838
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.01545278  0.28018159
## sample estimates:
##       cor 
## 0.1353763

cor.test(similarity_temp[["low_incorrect_avg"]]$X8,data_to_plot$XDFR_MRI_ACC_L3)

## 
##  Pearson's product-moment correlation
## 
## data:  similarity_temp[["low_incorrect_avg"]]$X8 and data_to_plot$XDFR_MRI_ACC_L3
## t = -0.5045, df = 111, p-value = 0.6149
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.2305234  0.1381198
## sample estimates:
##         cor 
## -0.04783045

Probe Period

There’s a correlation between accuracy and high load trials (regardless of accuracy) and omnibus span and high load trials (regardless of accuracy).

(scatter_plots_probe[[1]][["omnibus"]] + scatter_plots_probe[[2]][["omnibus"]]) /
  (scatter_plots_probe[[3]][["omnibus"]] + scatter_plots_probe[[4]][["omnibus"]])+
  plot_layout(guides="collect")+
  plot_annotation(title = "Omnibus span vs inter-trial similiarity - probe")

## Warning: Removed 57 rows containing non-finite values (stat_smooth).

## Warning: Removed 57 rows containing missing values (geom_point).

cor.test(similarity_temp[["high_correct_avg"]]$X11,data_to_plot$omnibus_span_no_DFR_MRI)

## 
##  Pearson's product-moment correlation
## 
## data:  similarity_temp[["high_correct_avg"]]$X11 and data_to_plot$omnibus_span_no_DFR_MRI
## t = 2.3836, df = 168, p-value = 0.01826
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.03119825 0.32259561
## sample estimates:
##       cor 
## 0.1808632

cor.test(similarity_temp[["high_incorrect_avg"]]$X11,data_to_plot$omnibus_span_no_DFR_MRI)

## 
##  Pearson's product-moment correlation
## 
## data:  similarity_temp[["high_incorrect_avg"]]$X11 and data_to_plot$omnibus_span_no_DFR_MRI
## t = 2.2925, df = 168, p-value = 0.02312
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.02428737 0.31638537
## sample estimates:
##       cor 
## 0.1741649

(scatter_plots_probe[[1]][["BPRS"]] + scatter_plots_probe[[2]][["BPRS"]]) /
  (scatter_plots_probe[[3]][["BPRS"]] + scatter_plots_probe[[4]][["BPRS"]])+
  plot_layout(guides="collect")+
  plot_annotation(title = "BPRS vs inter-trial similiarity - probe")

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 58 rows containing non-finite values (stat_smooth).

## Warning: Removed 58 rows containing missing values (geom_point).

cor.test(similarity_temp[["low_correct_avg"]]$X11,data_to_plot$BPRS_TOT.x)

## 
##  Pearson's product-moment correlation
## 
## data:  similarity_temp[["low_correct_avg"]]$X11 and data_to_plot$BPRS_TOT.x
## t = 1.2655, df = 167, p-value = 0.2075
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.05429678  0.24482013
## sample estimates:
##        cor 
## 0.09746211

(scatter_plots_probe[[1]][["L3_acc"]] + scatter_plots_probe[[2]][["L3_acc"]]) /
  (scatter_plots_probe[[3]][["L3_acc"]] + scatter_plots_probe[[4]][["L3_acc"]])+
  plot_layout(guides="collect")+
  plot_annotation(title = "L3_acc vs inter-trial similiarity - probe")

## Warning: Removed 57 rows containing non-finite values (stat_smooth).

## Warning: Removed 57 rows containing missing values (geom_point).

cor.test(similarity_temp[["high_correct_avg"]]$X11,data_to_plot$XDFR_MRI_ACC_L3)

## 
##  Pearson's product-moment correlation
## 
## data:  similarity_temp[["high_correct_avg"]]$X11 and data_to_plot$XDFR_MRI_ACC_L3
## t = 4.6826, df = 168, p-value = 5.814e-06
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1994625 0.4664362
## sample estimates:
##      cor 
## 0.339776

cor.test(similarity_temp[["high_incorrect_avg"]]$X11,data_to_plot$XDFR_MRI_ACC_L3)

## 
##  Pearson's product-moment correlation
## 
## data:  similarity_temp[["high_incorrect_avg"]]$X11 and data_to_plot$XDFR_MRI_ACC_L3
## t = 2.4804, df = 168, p-value = 0.01411
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.03852978 0.32915653
## sample estimates:
##      cor 
## 0.187954

cor.test(similarity_temp[["low_correct_avg"]]$X11,data_to_plot$XDFR_MRI_ACC_L3)

## 
##  Pearson's product-moment correlation
## 
## data:  similarity_temp[["low_correct_avg"]]$X11 and data_to_plot$XDFR_MRI_ACC_L3
## t = -0.2819, df = 168, p-value = 0.7784
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.1716965  0.1291929
## sample estimates:
##         cor 
## -0.02174414

cor.test(similarity_temp[["low_incorrect_avg"]]$X11,data_to_plot$XDFR_MRI_ACC_L3)

## 
##  Pearson's product-moment correlation
## 
## data:  similarity_temp[["low_incorrect_avg"]]$X11 and data_to_plot$XDFR_MRI_ACC_L3
## t = 0.81138, df = 111, p-value = 0.4189
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.1094975  0.2578577
## sample estimates:
##        cor 
## 0.07678552

Comparing encoding to delay

Same analysis pattern as before we’re going to now try to correlate across TRs.

encoding_to_delay_plots <- list()

for (i in c(6,10,12)){
  colnames(similarity_temp[[i]]) <- unlist(similarity_temp[[1]][[1]])
  similarity_temp[[i]][similarity_temp[[i]] == 0] <- NA
  temp_plot_data <- cbind.data.frame(data_to_plot,similarity_temp[[i]])
  
  encoding_to_delay_plots[[names(similarity_temp)[i]]][["omnibus"]][["low_load_incorrect"]] <- ggplot(data = temp_plot_data,aes(x=omnibus_span_no_DFR_MRI,y=`low load incorrect`))+
    geom_point()+
    stat_smooth(method="lm")+
    ylab("Similarity")+
    ggtitle("Low Load - Incorrect trials")+
    theme_classic()
  
  encoding_to_delay_plots[[names(similarity_temp)[i]]][["omnibus"]][["high_load_incorrect"]] <- ggplot(data = temp_plot_data,aes(x=omnibus_span_no_DFR_MRI,y=`high load incorrect`))+
    geom_point()+
    stat_smooth(method="lm")+
    ylab("Similarity")+
    ggtitle("High Load - Incorrect trials")+
    theme_classic()
  
  encoding_to_delay_plots[[names(similarity_temp)[i]]][["omnibus"]][["low_load_correct"]] <- ggplot(data = temp_plot_data,aes(x=omnibus_span_no_DFR_MRI,y=`low load correct`))+
    geom_point()+
    stat_smooth(method="lm")+
    ylab("Similarity")+
    ggtitle("Low Load - Correct trials")+
    theme_classic()
  
  encoding_to_delay_plots[[names(similarity_temp)[i]]][["omnibus"]][["high_load_correct"]] <- ggplot(data = temp_plot_data,aes(x=omnibus_span_no_DFR_MRI,y=`high load correct`))+
    geom_point()+
    stat_smooth(method="lm")+
    ylab("Similarity")+
    ggtitle("High Load - Correct trials")+
    theme_classic()
  
  encoding_to_delay_plots[[names(similarity_temp)[i]]][["L3_Acc"]][["low_load_incorrect"]] <- ggplot(data = temp_plot_data,aes(x=XDFR_MRI_ACC_L3,y=`low load incorrect`))+
    geom_point()+
    stat_smooth(method="lm")+
    ylab("Similarity")+
    ggtitle("Low Load - Incorrect trials")+
    theme_classic()
  
  encoding_to_delay_plots[[names(similarity_temp)[i]]][["L3_Acc"]][["high_load_incorrect"]] <- ggplot(data = temp_plot_data,aes(x=XDFR_MRI_ACC_L3,y=`high load incorrect`))+
    geom_point()+
    stat_smooth(method="lm")+
    ylab("Similarity")+
    ggtitle("High Load - Incorrect trials")+
    theme_classic()
  
  encoding_to_delay_plots[[names(similarity_temp)[i]]][["L3_Acc"]][["low_load_correct"]] <- ggplot(data = temp_plot_data,aes(x=XDFR_MRI_ACC_L3,y=`low load correct`))+
    geom_point()+
    stat_smooth(method="lm")+
    ylab("Similarity")+
    ggtitle("Low Load - Correct trials")+
    theme_classic()
  
  encoding_to_delay_plots[[names(similarity_temp)[i]]][["L3_Acc"]][["high_load_correct"]] <- ggplot(data = temp_plot_data,aes(x=XDFR_MRI_ACC_L3,y=`high load correct`))+
    geom_point()+
    stat_smooth(method="lm")+
    ylab("Similarity")+
    ggtitle("High Load - Correct trials")+
    theme_classic()
  
  encoding_to_delay_plots[[names(similarity_temp)[i]]][["BPRS"]][["low_load_incorrect"]] <- ggplot(data = temp_plot_data,aes(x=BPRS_TOT.x,y=`low load incorrect`))+
    geom_point()+
    stat_smooth(method="lm")+
    ylab("Similarity")+
    ggtitle("Low Load - Incorrect trials")+
    theme_classic()
  
  encoding_to_delay_plots[[names(similarity_temp)[i]]][["BPRS"]][["high_load_incorrect"]] <- ggplot(data = temp_plot_data,aes(x=BPRS_TOT.x,y=`high load incorrect`))+
    geom_point()+
    stat_smooth(method="lm")+
    ylab("Similarity")+
    ggtitle("High Load - Incorrect trials")+
    theme_classic()
  
  encoding_to_delay_plots[[names(similarity_temp)[i]]][["BPRS"]][["low_load_correct"]] <- ggplot(data = temp_plot_data,aes(x=BPRS_TOT.x,y=`low load correct`))+
    geom_point()+
    stat_smooth(method="lm")+
    ylab("Similarity")+
    ggtitle("Low Load - Correct trials")+
    theme_classic()
  
  encoding_to_delay_plots[[names(similarity_temp)[i]]][["BPRS"]][["high_load_correct"]] <- ggplot(data = temp_plot_data,aes(x=BPRS_TOT.x,y=`high load correct`))+
    geom_point()+
    stat_smooth(method="lm")+
    ylab("Similarity")+
    ggtitle("High Load - Correct trials")+
    theme_classic()
}

temp_plot_data <- cbind.data.frame(data_to_plot,similarity_temp[["correct_encoding_to_correct_delay"]])
colnames(temp_plot_data)[9] <- "correct_encoding_delay"

encoding_to_delay_plots[["correct_encoding_to_correct_delay"]][["omnibus"]] <- ggplot(data = 
                                                                                        temp_plot_data,aes(x=omnibus_span_no_DFR_MRI,y=correct_encoding_delay))+
  geom_point()+
  stat_smooth(method="lm")+
  ylab("Similarity")+
  ggtitle("Template encoding/delay vs Omnibus Span")+
  theme_classic()

encoding_to_delay_plots[["correct_encoding_to_correct_delay"]][["BPRS"]] <- ggplot(data = 
                                                                                     temp_plot_data,aes(x=BPRS_TOT.x,y=correct_encoding_delay))+
  geom_point()+
  stat_smooth(method="lm")+
  ylab("Similarity")+
  ggtitle("Template encoding/delay vs BPRS")+
  theme_classic()

encoding_to_delay_plots[["correct_encoding_to_correct_delay"]][["L3_Acc"]] <- ggplot(data = 
                                                                                       temp_plot_data,aes(x=XDFR_MRI_ACC_L3,y=correct_encoding_delay))+
  geom_point()+
  stat_smooth(method="lm")+
  ylab("Similarity")+
  ggtitle("Template encoding/delay vs L3 accuracy")+
  theme_classic()

Just like before, black represents a statistically significant value (p < 0.05). Scatter plots will be shown below.

correlations = list()

for (i in c(6,10,12)){
  colnames(similarity_temp[[i]]) <- unlist(similarity_temp[[1]][[1]])
  temp_list <- list(r = data.frame(matrix(nrow=4,ncol=6)), p = data.frame(matrix(nrow=4,ncol=6)))
  
  for (behav in seq.int(2,7)){
    for (sim in seq.int(1,4)){ 
      temp_corr <- cor.test(similarity_temp[[i]][,sim],data_to_plot[,behav])
      temp_list[["r"]][sim,behav-1] <- temp_corr$estimate
      temp_list[["p"]][sim,behav-1] <- temp_corr$p.value
    }
  }
  
  colnames(temp_list[["r"]]) <- colnames(data_to_plot)[2:7]
  rownames(temp_list[["r"]]) <- colnames(similarity_temp[[i]])
  colnames(temp_list[["p"]]) <- colnames(data_to_plot)[2:7]
  rownames(temp_list[["p"]]) <- colnames(similarity_temp[[i]])
  
  
  correlations[[names(similarity_temp)[i]]] <- temp_list
  
}
temp <- data.frame(r=matrix(nrow=6,ncol=1),p=matrix(nrow=6,ncol=1))
rownames(temp) <- colnames(data_to_plot)[2:7]


for (behav in seq.int(2,7)){
  temp_corr <- cor.test(similarity_temp[["correct_encoding_to_correct_delay"]],data_to_plot[,behav])
  temp$r[behav-1] <- temp_corr$estimate
  temp$p[behav-2] <- temp_corr$p.value
}

correlations[["correct_encoding_to_correct_delay"]] <- temp

Individual Encoding to Individual Delay

There is a statistically significant negative correlation with high load accuracy and correct trials (regardless of load), and a trend towards a negative correlation between omnibus span and similiarity on correct low load trials. There is also a significant relationship between omnibus span and similarity on low load incorrect trials, but just as before, this needs to be taken with a grain of caution. There are no significant correlations with clinical measures.

correlations[["encoding_to_delay_avg"]][["r"]] %>% 
  mutate(
    condition = row.names(.),
    omnibus_span_no_DFR_MRI = cell_spec(omnibus_span_no_DFR_MRI, "html", 
                                        color =ifelse(correlations[["encoding_to_delay_avg"]][["p"]]$omnibus_span_no_DFR_MRI < 0.05, "black", "grey")),
    XDFR_MRI_ACC_L3 = cell_spec(XDFR_MRI_ACC_L3, "html", 
                                color =ifelse(correlations[["encoding_to_delay_avg"]][["p"]]$XDFR_MRI_ACC_L3 < 0.05, "black", "grey")),
    BPRS_TOT.x = cell_spec(BPRS_TOT.x, "html", 
                           color =ifelse(correlations[["encoding_to_delay_avg"]][["p"]]$BPRS_TOT.x < 0.05, "black", "grey"))
  ) %>% 
  select(condition,omnibus_span_no_DFR_MRI,XDFR_MRI_ACC_L3,BPRS_TOT.x) %>%
  kable(format = "html", escape = F) %>%
  kable_styling("striped", full_width = F) %>% 
  add_header_above((c(" ", "Individual Encoding to Individual Delay" = 3)))

	Individual Encoding to Individual Delay
condition	omnibus_span_no_DFR_MRI	XDFR_MRI_ACC_L3	BPRS_TOT.x
low load incorrect	-0.131003807047542	0.00201704162365899	-0.101112090700478
high load incorrect	0.0756928888526784	-0.0751276248534951	0.0186020785742466
low load correct	-0.14459654921975	-0.183072274547306	-0.0331690589880607
high load correct	-0.00177587951246186	-0.18548145471416	-0.0176013091545108

This measure can distiniguish load but not accuracy.

encoding_to_delay_avg <- fisherz(similarity_temp[["encoding_to_delay_avg"]])

t.test(encoding_to_delay_avg[,4],encoding_to_delay_avg[,2],paired=TRUE)

## 
##  Paired t-test
## 
## data:  encoding_to_delay_avg[, 4] and encoding_to_delay_avg[, 2]
## t = -0.23475, df = 169, p-value = 0.8147
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.01993260  0.01569585
## sample estimates:
## mean of the differences 
##            -0.002118377

t.test(encoding_to_delay_avg[,4],encoding_to_delay_avg[,3],paired=TRUE)

## 
##  Paired t-test
## 
## data:  encoding_to_delay_avg[, 4] and encoding_to_delay_avg[, 3]
## t = 11.73, df = 169, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  0.08900045 0.12501985
## sample estimates:
## mean of the differences 
##               0.1070102

(encoding_to_delay_plots[["encoding_to_delay_avg"]][["omnibus"]][["high_load_correct"]] + encoding_to_delay_plots[["encoding_to_delay_avg"]][["omnibus"]][["high_load_incorrect"]]) /
  (encoding_to_delay_plots[["encoding_to_delay_avg"]][["omnibus"]][["low_load_correct"]] + 
     encoding_to_delay_plots[["encoding_to_delay_avg"]][["omnibus"]][["low_load_incorrect"]])+
  plot_annotation(title = "Individual trials vs omnibus")

## Warning: Removed 57 rows containing non-finite values (stat_smooth).

## Warning: Removed 57 rows containing missing values (geom_point).

(encoding_to_delay_plots[["encoding_to_delay_avg"]][["L3_Acc"]][["high_load_correct"]] + encoding_to_delay_plots[["encoding_to_delay_avg"]][["L3_Acc"]][["high_load_incorrect"]]) /
  (encoding_to_delay_plots[["encoding_to_delay_avg"]][["L3_Acc"]][["low_load_correct"]] + 
     encoding_to_delay_plots[["encoding_to_delay_avg"]][["L3_Acc"]][["low_load_incorrect"]])+
  plot_annotation(title = "Individual trials vs High Load Accuracy")

## Warning: Removed 57 rows containing non-finite values (stat_smooth).

## Warning: Removed 57 rows containing missing values (geom_point).

(encoding_to_delay_plots[["encoding_to_delay_avg"]][["BPRS"]][["high_load_correct"]] + encoding_to_delay_plots[["encoding_to_delay_avg"]][["BPRS"]][["high_load_incorrect"]]) /
  (encoding_to_delay_plots[["encoding_to_delay_avg"]][["BPRS"]][["low_load_correct"]] + 
     encoding_to_delay_plots[["encoding_to_delay_avg"]][["BPRS"]][["low_load_incorrect"]])+
  plot_annotation(title = "Individual trials vs BPRS Total")

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 58 rows containing non-finite values (stat_smooth).

## Warning: Removed 58 rows containing missing values (geom_point).

Individual Trial Encoding vs Correct Template Delay

Here, we start to see a different pattern from the fusiform. There is a significant negative correlation between similarity on correct high load trials and omnibus span and also with high load accuracy. There is also no significant relationship with clinical measures.

This is slightly different from the fusiform, where we saw relationships with clinical measures, and where we didn’t see any significant relationships with omnibus span.

correlations[["encoding_to_correct_delay_avg"]][["r"]] %>% 
  mutate(
    condition = row.names(.),
    omnibus_span_no_DFR_MRI = cell_spec(omnibus_span_no_DFR_MRI, "html", 
                                        color =ifelse(correlations[["encoding_to_correct_delay_avg"]][["p"]]$omnibus_span_no_DFR_MRI < 0.05, "black", "grey")),
    XDFR_MRI_ACC_L3 = cell_spec(XDFR_MRI_ACC_L3, "html", 
                                color =ifelse(correlations[["encoding_to_correct_delay_avg"]][["p"]]$XDFR_MRI_ACC_L3 < 0.05, "black", "grey")),
    BPRS_TOT.x = cell_spec(BPRS_TOT.x, "html", 
                           color =ifelse(correlations[["encoding_to_correct_delay_avg"]][["p"]]$BPRS_TOT.x < 0.05, "black", "grey"))
  ) %>% 
  select(condition,omnibus_span_no_DFR_MRI,XDFR_MRI_ACC_L3,BPRS_TOT.x) %>%
  kable(format = "html", escape = F) %>%
  kable_styling("striped", full_width = F) %>% 
  add_header_above((c(" ", "Individual Encoding to Template Delay" = 3)))

	Individual Encoding to Template Delay
condition	omnibus_span_no_DFR_MRI	XDFR_MRI_ACC_L3	BPRS_TOT.x
low load incorrect	-0.00442964778690035	-0.00610835379911364	0.0203239795220361
high load incorrect	-0.036929537132356	-0.0403694255449535	-0.0558826589752957
low load correct	0.0401578811970275	-0.0679254364734098	-0.0279882685192926
high load correct	-0.184028837558584	-0.158219670027366	-0.00220693705890965

This measure can distinguish both load and accuracy.

encoding_to_correct_delay_avg <- fisherz(similarity_temp[["encoding_to_correct_delay_avg"]])

t.test(encoding_to_correct_delay_avg[,4],encoding_to_correct_delay_avg[,2],paired=TRUE)

## 
##  Paired t-test
## 
## data:  encoding_to_correct_delay_avg[, 4] and encoding_to_correct_delay_avg[, 2]
## t = -2.5283, df = 169, p-value = 0.01238
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.034415229 -0.004236351
## sample estimates:
## mean of the differences 
##             -0.01932579

t.test(encoding_to_correct_delay_avg[,4],encoding_to_correct_delay_avg[,3],paired=TRUE)

## 
##  Paired t-test
## 
## data:  encoding_to_correct_delay_avg[, 4] and encoding_to_correct_delay_avg[, 3]
## t = -7.7977, df = 169, p-value = 6.18e-13
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.08973715 -0.05347964
## sample estimates:
## mean of the differences 
##              -0.0716084

(encoding_to_delay_plots[["encoding_to_correct_delay_avg"]][["omnibus"]][["high_load_correct"]] + encoding_to_delay_plots[["encoding_to_correct_delay_avg"]][["omnibus"]][["high_load_incorrect"]]) /
  (encoding_to_delay_plots[["encoding_to_correct_delay_avg"]][["omnibus"]][["low_load_correct"]] + 
     encoding_to_delay_plots[["encoding_to_correct_delay_avg"]][["omnibus"]][["low_load_incorrect"]])+
  plot_annotation(title = "Individual trials vs omnibus")

## Warning: Removed 57 rows containing non-finite values (stat_smooth).

## Warning: Removed 57 rows containing missing values (geom_point).

(encoding_to_delay_plots[["encoding_to_correct_delay_avg"]][["L3_Acc"]][["high_load_correct"]] + encoding_to_delay_plots[["encoding_to_correct_delay_avg"]][["L3_Acc"]][["high_load_incorrect"]]) /
  (encoding_to_delay_plots[["encoding_to_correct_delay_avg"]][["L3_Acc"]][["low_load_correct"]] + 
     encoding_to_delay_plots[["encoding_to_correct_delay_avg"]][["L3_Acc"]][["low_load_incorrect"]])+
  plot_annotation(title = "Individual trials vs High Load Accuracy")

## Warning: Removed 57 rows containing non-finite values (stat_smooth).

## Warning: Removed 57 rows containing missing values (geom_point).

(encoding_to_delay_plots[["encoding_to_correct_delay_avg"]][["BPRS"]][["high_load_correct"]] + encoding_to_delay_plots[["encoding_to_correct_delay_avg"]][["BPRS"]][["high_load_incorrect"]]) /
  (encoding_to_delay_plots[["encoding_to_correct_delay_avg"]][["BPRS"]][["low_load_correct"]] + 
     encoding_to_delay_plots[["encoding_to_correct_delay_avg"]][["BPRS"]][["low_load_incorrect"]])+
  plot_annotation(title = "Individual trials vs BPRS Total")

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 58 rows containing non-finite values (stat_smooth).

## Warning: Removed 58 rows containing missing values (geom_point).

Correct Template Encoding vs Individual Trial Delay

Here, we only see a significant negative correlation between similarity at correct low load trials and high load accuracy. This is starkly different from the fusiform similarity, where we saw relationships between similarity at correct high load trials and omnibus span and BPRS. Also interesting here is that all trial types showed correlation between fusiform similarity and high load accuracy.

correlations[["correct_encoding_to_delay_avg"]][["r"]] %>% 
  mutate(
    condition = row.names(.),
    omnibus_span_no_DFR_MRI = cell_spec(omnibus_span_no_DFR_MRI, "html", 
                                        color =ifelse(correlations[["correct_encoding_to_delay_avg"]][["p"]]$omnibus_span_no_DFR_MRI < 0.05, "black", "grey")),
    XDFR_MRI_ACC_L3 = cell_spec(XDFR_MRI_ACC_L3, "html", 
                                color =ifelse(correlations[["correct_encoding_to_delay_avg"]][["p"]]$XDFR_MRI_ACC_L3 < 0.05, "black", "grey")),
    BPRS_TOT.x = cell_spec(BPRS_TOT.x, "html", 
                           color =ifelse(correlations[["correct_encoding_to_delay_avg"]][["p"]]$BPRS_TOT.x < 0.05, "black", "grey"))
  ) %>% 
  select(condition,omnibus_span_no_DFR_MRI,XDFR_MRI_ACC_L3,BPRS_TOT.x) %>%
  kable(format = "html", escape = F) %>%
  kable_styling("striped", full_width = F) %>% 
  add_header_above((c(" ", "Template Encoding to Delay" = 3)))

	Template Encoding to Delay
condition	omnibus_span_no_DFR_MRI	XDFR_MRI_ACC_L3	BPRS_TOT.x
low load incorrect	-0.142923394839187	-0.0628260628135304	-0.0408537364515526
high load incorrect	0.0759251542107984	0.0495684274120333	-0.0412615577186966
low load correct	-0.0928325164173533	-0.196674391063135	-0.00556839298229165
high load correct	0.000452855700311408	-0.043525624420269	-0.126213928286512

This measure can only distinguish load.

correct_encoding_to_delay_avg <- fisherz(similarity_temp[["correct_encoding_to_delay_avg"]])

t.test(correct_encoding_to_delay_avg[,4],correct_encoding_to_delay_avg[,2],paired=TRUE)

## 
##  Paired t-test
## 
## data:  correct_encoding_to_delay_avg[, 4] and correct_encoding_to_delay_avg[, 2]
## t = -1.2422, df = 169, p-value = 0.2159
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.026610708  0.006055906
## sample estimates:
## mean of the differences 
##              -0.0102774

t.test(correct_encoding_to_delay_avg[,4],correct_encoding_to_delay_avg[,3],paired=TRUE)

## 
##  Paired t-test
## 
## data:  correct_encoding_to_delay_avg[, 4] and correct_encoding_to_delay_avg[, 3]
## t = 5.6767, df = 169, p-value = 5.86e-08
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  0.03072772 0.06349347
## sample estimates:
## mean of the differences 
##              0.04711059

(encoding_to_delay_plots[["correct_encoding_to_delay_avg"]][["omnibus"]][["high_load_correct"]] + encoding_to_delay_plots[["correct_encoding_to_delay_avg"]][["omnibus"]][["high_load_incorrect"]]) /
  (encoding_to_delay_plots[["correct_encoding_to_delay_avg"]][["omnibus"]][["low_load_correct"]] + 
     encoding_to_delay_plots[["correct_encoding_to_delay_avg"]][["omnibus"]][["low_load_incorrect"]])+
  plot_annotation(title = "Individual trials vs omnibus")

## Warning: Removed 57 rows containing non-finite values (stat_smooth).

## Warning: Removed 57 rows containing missing values (geom_point).

(encoding_to_delay_plots[["correct_encoding_to_delay_avg"]][["L3_Acc"]][["high_load_correct"]] + encoding_to_delay_plots[["correct_encoding_to_delay_avg"]][["L3_Acc"]][["high_load_incorrect"]]) /
  (encoding_to_delay_plots[["correct_encoding_to_delay_avg"]][["L3_Acc"]][["low_load_correct"]] + 
     encoding_to_delay_plots[["correct_encoding_to_delay_avg"]][["L3_Acc"]][["low_load_incorrect"]])+
  plot_annotation(title = "Individual trials vs High Load Accuracy")

## Warning: Removed 57 rows containing non-finite values (stat_smooth).

## Warning: Removed 57 rows containing missing values (geom_point).

(encoding_to_delay_plots[["correct_encoding_to_delay_avg"]][["BPRS"]][["high_load_correct"]] + encoding_to_delay_plots[["correct_encoding_to_delay_avg"]][["BPRS"]][["high_load_incorrect"]]) /
  (encoding_to_delay_plots[["correct_encoding_to_delay_avg"]][["BPRS"]][["low_load_correct"]] + 
     encoding_to_delay_plots[["correct_encoding_to_delay_avg"]][["BPRS"]][["low_load_incorrect"]])+
  plot_annotation(title = "Individual trials vs BPRS Total")

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 58 rows containing non-finite values (stat_smooth).

## Warning: Removed 58 rows containing missing values (geom_point).

Correct template encoding to correct template delay

No significant relationships here - only thing that even trends is accuracy. This is very different from the fusiform, where there were significant relationships between similarity and all three behavioral measures.

correlations[["correct_encoding_to_correct_delay"]][c(2,3,5),] %>% 
  mutate(
    condition = row.names(.),
    r = cell_spec(r, "html", 
                  color =ifelse(p < 0.05, "black", "grey"))
  )  %>% 
  select(condition,r) %>%
  kable(format = "html", escape = F) %>%
  kable_styling("striped", full_width = F) %>%
  add_header_above(c(" ", "Template Encoding to Template Delay" = 1))

	Template Encoding to Template Delay
condition	r
omnibus_span_no_DFR_MRI	-0.0459543951859351
XDFR_MRI_ACC_L3	-0.141366038958814
BPRS_TOT.x	-0.0350881739554959

encoding_to_delay_plots[["correct_encoding_to_correct_delay"]][["omnibus"]]

encoding_to_delay_plots[["correct_encoding_to_correct_delay"]][["L3_Acc"]]

encoding_to_delay_plots[["correct_encoding_to_correct_delay"]][["BPRS"]]

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

Comparing encoding to probe

Now, let’s try encoding to probe. This measure does have any relationships with behavior, though some of the individual correlation measures can distinguish between high and low load trials.

encoding_to_probe_plots <- list()

for (i in c(7,11,13)){
  colnames(similarity_temp[[i]]) <- unlist(similarity_temp[[1]][[1]])
  similarity_temp[[i]][similarity_temp[[i]]==0] <- NA
  temp_plot_data <- cbind.data.frame(data_to_plot,similarity_temp[[i]])
  
  encoding_to_probe_plots[[names(similarity_temp)[i]]][["omnibus"]][["low_load_incorrect"]] <- ggplot(data = temp_plot_data,aes(x=omnibus_span_no_DFR_MRI,y=`low load incorrect`))+
    geom_point()+
    stat_smooth(method="lm")+
    ylab("Similarity")+
    ggtitle("Low Load - Incorrect trials")+
    theme_classic()
  
  encoding_to_probe_plots[[names(similarity_temp)[i]]][["omnibus"]][["high_load_incorrect"]] <- ggplot(data = temp_plot_data,aes(x=omnibus_span_no_DFR_MRI,y=`high load incorrect`))+
    geom_point()+
    stat_smooth(method="lm")+
    ylab("Similarity")+
    ggtitle("High Load - Incorrect trials")+
    theme_classic()
  
  encoding_to_probe_plots[[names(similarity_temp)[i]]][["omnibus"]][["low_load_correct"]] <- ggplot(data = temp_plot_data,aes(x=omnibus_span_no_DFR_MRI,y=`low load correct`))+
    geom_point()+
    stat_smooth(method="lm")+
    ylab("Similarity")+
    ggtitle("Low Load - Correct trials")+
    theme_classic()
  
  encoding_to_probe_plots[[names(similarity_temp)[i]]][["omnibus"]][["high_load_correct"]] <- ggplot(data = temp_plot_data,aes(x=omnibus_span_no_DFR_MRI,y=`high load correct`))+
    geom_point()+
    stat_smooth(method="lm")+
    ylab("Similarity")+
    ggtitle("High Load - Correct trials")+
    theme_classic()
  
  encoding_to_probe_plots[[names(similarity_temp)[i]]][["L3_Acc"]][["low_load_incorrect"]] <- ggplot(data = temp_plot_data,aes(x=XDFR_MRI_ACC_L3,y=`low load incorrect`))+
    geom_point()+
    stat_smooth(method="lm")+
    ylab("Similarity")+
    ggtitle("Low Load - Incorrect trials")+
    theme_classic()
  
  encoding_to_probe_plots[[names(similarity_temp)[i]]][["L3_Acc"]][["high_load_incorrect"]] <- ggplot(data = temp_plot_data,aes(x=XDFR_MRI_ACC_L3,y=`high load incorrect`))+
    geom_point()+
    stat_smooth(method="lm")+
    ylab("Similarity")+
    ggtitle("High Load - Incorrect trials")+
    theme_classic()
  
  encoding_to_probe_plots[[names(similarity_temp)[i]]][["L3_Acc"]][["low_load_correct"]] <- ggplot(data = temp_plot_data,aes(x=XDFR_MRI_ACC_L3,y=`low load correct`))+
    geom_point()+
    stat_smooth(method="lm")+
    ylab("Similarity")+
    ggtitle("Low Load - Correct trials")+
    theme_classic()
  
  encoding_to_probe_plots[[names(similarity_temp)[i]]][["L3_Acc"]][["high_load_correct"]] <- ggplot(data = temp_plot_data,aes(x=XDFR_MRI_ACC_L3,y=`high load correct`))+
    geom_point()+
    stat_smooth(method="lm")+
    ylab("Similarity")+
    ggtitle("High Load - Correct trials")+
    theme_classic()
  
  encoding_to_probe_plots[[names(similarity_temp)[i]]][["BPRS"]][["low_load_incorrect"]] <- ggplot(data = temp_plot_data,aes(x=BPRS_TOT.x,y=`low load incorrect`))+
    geom_point()+
    stat_smooth(method="lm")+
    ylab("Similarity")+
    ggtitle("Low Load - Incorrect trials")+
    theme_classic()
  
  encoding_to_probe_plots[[names(similarity_temp)[i]]][["BPRS"]][["high_load_incorrect"]] <- ggplot(data = temp_plot_data,aes(x=BPRS_TOT.x,y=`high load incorrect`))+
    geom_point()+
    stat_smooth(method="lm")+
    ylab("Similarity")+
    ggtitle("High Load - Incorrect trials")+
    theme_classic()
  
  encoding_to_probe_plots[[names(similarity_temp)[i]]][["BPRS"]][["low_load_correct"]] <- ggplot(data = temp_plot_data,aes(x=BPRS_TOT.x,y=`low load correct`))+
    geom_point()+
    stat_smooth(method="lm")+
    ylab("Similarity")+
    ggtitle("Low Load - Correct trials")+
    theme_classic()
  
  encoding_to_probe_plots[[names(similarity_temp)[i]]][["BPRS"]][["high_load_correct"]] <- ggplot(data = temp_plot_data,aes(x=BPRS_TOT.x,y=`high load correct`))+
    geom_point()+
    stat_smooth(method="lm")+
    ylab("Similarity")+
    ggtitle("High Load - Correct trials")+
    theme_classic()
}

temp_plot_data <- cbind.data.frame(data_to_plot,similarity_temp[["correct_encoding_to_correct_probe"]])
colnames(temp_plot_data)[9] <- "correct_encoding_probe"

encoding_to_probe_plots[["correct_encoding_to_correct_probe"]][["omnibus"]] <- ggplot(data = 
                                                                                        temp_plot_data,aes(x=omnibus_span_no_DFR_MRI,y=correct_encoding_probe))+
  geom_point()+
  stat_smooth(method="lm")+
  ylab("Similarity")+
  ggtitle("Template encoding/probe vs Omnibus Span")+
  theme_classic()

encoding_to_probe_plots[["correct_encoding_to_correct_probe"]][["BPRS"]] <- ggplot(data = 
                                                                                     temp_plot_data,aes(x=BPRS_TOT.x,y=correct_encoding_probe))+
  geom_point()+
  stat_smooth(method="lm")+
  ylab("Similarity")+
  ggtitle("Template encoding/probe vs BPRS")+
  theme_classic()

encoding_to_probe_plots[["correct_encoding_to_correct_probe"]][["L3_Acc"]] <- ggplot(data = 
                                                                                       temp_plot_data,aes(x=XDFR_MRI_ACC_L3,y=correct_encoding_probe))+
  geom_point()+
  stat_smooth(method="lm")+
  ylab("Similarity")+
  ggtitle("Template encoding/probe vs L3 accuracy")+
  theme_classic()

In these, graphs, if a correlation is black, it is below p < 0.05; if it is not, it is grey.

correlations = list()

for (i in c(7,11,13)){
  colnames(similarity_temp[[i]]) <- unlist(similarity_temp[[1]][[1]])
  temp_list <- list(r = data.frame(matrix(nrow=4,ncol=6)), p = data.frame(matrix(nrow=4,ncol=6)))
  
  for (behav in seq.int(2,7)){
    for (sim in seq.int(1,4)){ 
      temp_corr <- cor.test(similarity_temp[[i]][,sim],data_to_plot[,behav])
      temp_list[["r"]][sim,behav-1] <- temp_corr$estimate
      temp_list[["p"]][sim,behav-1] <- temp_corr$p.value
    }
  }
  
  colnames(temp_list[["r"]]) <- colnames(data_to_plot)[2:7]
  rownames(temp_list[["r"]]) <- colnames(similarity_temp[[i]])
  colnames(temp_list[["p"]]) <- colnames(data_to_plot)[2:7]
  rownames(temp_list[["p"]]) <- colnames(similarity_temp[[i]])
  
  
  correlations[[names(similarity_temp)[i]]] <- temp_list
  
}
temp <- data.frame(r=matrix(nrow=6,ncol=1),p=matrix(nrow=6,ncol=1))
rownames(temp) <- colnames(data_to_plot)[2:7]


for (behav in seq.int(2,7)){
  temp_corr <- cor.test(similarity_temp[["correct_encoding_to_correct_probe"]],data_to_plot[,behav])
  temp$r[behav-1] <- temp_corr$estimate
  temp$p[behav-2] <- temp_corr$p.value
}

correlations[["correct_encoding_to_correct_probe"]] <- temp

Individual Encoding to Individual Probe

No correlations here.

correlations[["encoding_to_probe_avg"]][["r"]] %>% 
  mutate(
    condition = row.names(.),
    omnibus_span_no_DFR_MRI = cell_spec(omnibus_span_no_DFR_MRI, "html", 
                                        color =ifelse(correlations[["encoding_to_probe_avg"]][["p"]]$omnibus_span_no_DFR_MRI < 0.05, "black", "grey")),
    XDFR_MRI_ACC_L3 = cell_spec(XDFR_MRI_ACC_L3, "html", 
                                color =ifelse(correlations[["encoding_to_probe_avg"]][["p"]]$XDFR_MRI_ACC_L3 < 0.05, "black", "grey")),
    BPRS_TOT.x = cell_spec(BPRS_TOT.x, "html", 
                           color =ifelse(correlations[["encoding_to_probe_avg"]][["p"]]$BPRS_TOT.x < 0.05, "black", "grey"))
  ) %>% 
  select(condition,omnibus_span_no_DFR_MRI,XDFR_MRI_ACC_L3,BPRS_TOT.x) %>%
  kable(format = "html", escape = F) %>%
  kable_styling("striped", full_width = F) %>% 
  add_header_above((c(" ", "Individual Encoding to Individual Probe" = 3)))

	Individual Encoding to Individual Probe
condition	omnibus_span_no_DFR_MRI	XDFR_MRI_ACC_L3	BPRS_TOT.x
low load incorrect	-0.11308500810141	-0.0541219414163688	0.103429693788007
high load incorrect	-0.11396284256804	0.0182947763540957	-0.0340578810731565
low load correct	-0.132274001447299	-0.0397258232481695	0.103288755943387
high load correct	0.0152588450734783	0.106918484955607	0.127349181899464

This measure can distinguish between load but not accuracy.

encoding_to_probe_avg <- fisherz(similarity_temp[["encoding_to_probe_avg"]])

t.test(encoding_to_probe_avg[,4],encoding_to_probe_avg[,2],paired=TRUE)

## 
##  Paired t-test
## 
## data:  encoding_to_probe_avg[, 4] and encoding_to_probe_avg[, 2]
## t = 0.28594, df = 169, p-value = 0.7753
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.01497926  0.02005367
## sample estimates:
## mean of the differences 
##             0.002537202

t.test(encoding_to_probe_avg[,4],encoding_to_probe_avg[,3],paired=TRUE)

## 
##  Paired t-test
## 
## data:  encoding_to_probe_avg[, 4] and encoding_to_probe_avg[, 3]
## t = 5.3051, df = 169, p-value = 3.493e-07
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  0.02220810 0.04853148
## sample estimates:
## mean of the differences 
##              0.03536979

(encoding_to_probe_plots[["encoding_to_probe_avg"]][["omnibus"]][["high_load_correct"]] + encoding_to_probe_plots[["encoding_to_probe_avg"]][["omnibus"]][["high_load_incorrect"]]) /
  (encoding_to_probe_plots[["encoding_to_probe_avg"]][["omnibus"]][["low_load_correct"]] + 
     encoding_to_probe_plots[["encoding_to_probe_avg"]][["omnibus"]][["low_load_incorrect"]])+
  plot_annotation(title = "Individual trials vs omnibus")

## Warning: Removed 57 rows containing non-finite values (stat_smooth).

## Warning: Removed 57 rows containing missing values (geom_point).

(encoding_to_probe_plots[["encoding_to_probe_avg"]][["L3_Acc"]][["high_load_correct"]] + encoding_to_probe_plots[["encoding_to_probe_avg"]][["L3_Acc"]][["high_load_incorrect"]]) /
  (encoding_to_probe_plots[["encoding_to_probe_avg"]][["L3_Acc"]][["low_load_correct"]] + 
     encoding_to_probe_plots[["encoding_to_probe_avg"]][["L3_Acc"]][["low_load_incorrect"]])+
  plot_annotation(title = "Individual trials vs High Load Accuracy")

## Warning: Removed 57 rows containing non-finite values (stat_smooth).

## Warning: Removed 57 rows containing missing values (geom_point).

(encoding_to_probe_plots[["encoding_to_probe_avg"]][["BPRS"]][["high_load_correct"]] + encoding_to_probe_plots[["encoding_to_probe_avg"]][["BPRS"]][["high_load_incorrect"]]) /
  (encoding_to_probe_plots[["encoding_to_probe_avg"]][["BPRS"]][["low_load_correct"]] + 
     encoding_to_probe_plots[["encoding_to_probe_avg"]][["BPRS"]][["low_load_incorrect"]])+
  plot_annotation(title = "Individual trials vs BPRS Total")

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 58 rows containing non-finite values (stat_smooth).

## Warning: Removed 58 rows containing missing values (geom_point).

Individual Trial Encoding vs Correct Template Probe

Significant correlation between similarity at the low load correct trials and span.

correlations[["encoding_to_correct_probe_avg"]][["r"]] %>% 
  mutate(
    condition = row.names(.),
    omnibus_span_no_DFR_MRI = cell_spec(omnibus_span_no_DFR_MRI, "html", 
                                        color =ifelse(correlations[["encoding_to_correct_probe_avg"]][["p"]]$omnibus_span_no_DFR_MRI < 0.05, "black", "grey")),
    XDFR_MRI_ACC_L3 = cell_spec(XDFR_MRI_ACC_L3, "html", 
                                color =ifelse(correlations[["encoding_to_correct_probe_avg"]][["p"]]$XDFR_MRI_ACC_L3 < 0.05, "black", "grey")),
    BPRS_TOT.x = cell_spec(BPRS_TOT.x, "html", 
                           color =ifelse(correlations[["encoding_to_correct_probe_avg"]][["p"]]$BPRS_TOT.x < 0.05, "black", "grey"))
  ) %>% 
  select(condition,omnibus_span_no_DFR_MRI,XDFR_MRI_ACC_L3,BPRS_TOT.x) %>%
  kable(format = "html", escape = F) %>%
  kable_styling("striped", full_width = F) %>% 
  add_header_above((c(" ", "Individual Encoding to Template probe" = 3)))

	Individual Encoding to Template probe
condition	omnibus_span_no_DFR_MRI	XDFR_MRI_ACC_L3	BPRS_TOT.x
low load incorrect	-0.150066752033504	0.0531966246889002	-0.0201338877626089
high load incorrect	-0.0839435554221092	0.112049282067899	0.0958919786614344
low load correct	-0.183810479715138	-0.148667525923073	0.0188064366510966
high load correct	0.0164872329730542	0.053809718240409	0.101567168245189

This measure distinguishes load but not accuracy.

encoding_to_correct_probe_avg <- fisherz(similarity_temp[["encoding_to_correct_probe_avg"]])

t.test(encoding_to_correct_probe_avg[,4],encoding_to_correct_probe_avg[,2],paired=TRUE)

## 
##  Paired t-test
## 
## data:  encoding_to_correct_probe_avg[, 4] and encoding_to_correct_probe_avg[, 2]
## t = 0.85892, df = 169, p-value = 0.3916
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.008718501  0.022148526
## sample estimates:
## mean of the differences 
##             0.006715013

t.test(encoding_to_correct_probe_avg[,4],encoding_to_correct_probe_avg[,3],paired=TRUE)

## 
##  Paired t-test
## 
## data:  encoding_to_correct_probe_avg[, 4] and encoding_to_correct_probe_avg[, 3]
## t = 7.5967, df = 169, p-value = 1.98e-12
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  0.04235849 0.07210289
## sample estimates:
## mean of the differences 
##              0.05723069

(encoding_to_probe_plots[["encoding_to_correct_probe_avg"]][["omnibus"]][["high_load_correct"]] + encoding_to_probe_plots[["encoding_to_correct_probe_avg"]][["omnibus"]][["high_load_incorrect"]]) /
  (encoding_to_probe_plots[["encoding_to_correct_probe_avg"]][["omnibus"]][["low_load_correct"]] + 
     encoding_to_probe_plots[["encoding_to_correct_probe_avg"]][["omnibus"]][["low_load_incorrect"]])+
  plot_annotation(title = "Individual trials vs omnibus")

## Warning: Removed 57 rows containing non-finite values (stat_smooth).

## Warning: Removed 57 rows containing missing values (geom_point).

(encoding_to_probe_plots[["encoding_to_correct_probe_avg"]][["L3_Acc"]][["high_load_correct"]] + encoding_to_probe_plots[["encoding_to_correct_probe_avg"]][["L3_Acc"]][["high_load_incorrect"]]) /
  (encoding_to_probe_plots[["encoding_to_correct_probe_avg"]][["L3_Acc"]][["low_load_correct"]] + 
     encoding_to_probe_plots[["encoding_to_correct_probe_avg"]][["L3_Acc"]][["low_load_incorrect"]])+
  plot_annotation(title = "Individual trials vs High Load Accuracy")

## Warning: Removed 57 rows containing non-finite values (stat_smooth).

## Warning: Removed 57 rows containing missing values (geom_point).

(encoding_to_probe_plots[["encoding_to_correct_probe_avg"]][["BPRS"]][["high_load_correct"]] + encoding_to_probe_plots[["encoding_to_correct_probe_avg"]][["BPRS"]][["high_load_incorrect"]]) /
  (encoding_to_probe_plots[["encoding_to_correct_probe_avg"]][["BPRS"]][["low_load_correct"]] + 
     encoding_to_probe_plots[["encoding_to_correct_probe_avg"]][["BPRS"]][["low_load_incorrect"]])+
  plot_annotation(title = "Individual trials vs BPRS Total")

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 58 rows containing non-finite values (stat_smooth).

## Warning: Removed 58 rows containing missing values (geom_point).

Correct Template Encoding vs Individual Trial probe

No significant relationships.

correlations[["correct_encoding_to_probe_avg"]][["r"]] %>% 
  mutate(
    condition = row.names(.),
    omnibus_span_no_DFR_MRI = cell_spec(omnibus_span_no_DFR_MRI, "html", 
                                        color =ifelse(correlations[["correct_encoding_to_probe_avg"]][["p"]]$omnibus_span_no_DFR_MRI < 0.05, "black", "grey")),
    XDFR_MRI_ACC_L3 = cell_spec(XDFR_MRI_ACC_L3, "html", 
                                color =ifelse(correlations[["correct_encoding_to_probe_avg"]][["p"]]$XDFR_MRI_ACC_L3 < 0.05, "black", "grey")),
    BPRS_TOT.x = cell_spec(BPRS_TOT.x, "html", 
                           color =ifelse(correlations[["correct_encoding_to_probe_avg"]][["p"]]$BPRS_TOT.x < 0.05, "black", "grey"))
  ) %>% 
  select(condition,omnibus_span_no_DFR_MRI,XDFR_MRI_ACC_L3,BPRS_TOT.x) %>%
  kable(format = "html", escape = F) %>%
  kable_styling("striped", full_width = F) %>% 
  add_header_above((c(" ", "Template Encoding to probe" = 3)))

	Template Encoding to probe
condition	omnibus_span_no_DFR_MRI	XDFR_MRI_ACC_L3	BPRS_TOT.x
low load incorrect	-0.130476566628407	-0.170014618747209	0.159230940572967
high load incorrect	-0.0620210617698407	0.0855301466624124	0.0493015835643384
low load correct	-0.0937808861586807	-0.0468728356263741	0.0455312927087539
high load correct	-0.126196571387953	0.0143330465570246	0.0225574514433589

Not predictive of anything here.

correct_encoding_to_probe_avg <- fisherz(similarity_temp[["correct_encoding_to_probe_avg"]])

t.test(correct_encoding_to_probe_avg[,4],correct_encoding_to_probe_avg[,2],paired=TRUE)

## 
##  Paired t-test
## 
## data:  correct_encoding_to_probe_avg[, 4] and correct_encoding_to_probe_avg[, 2]
## t = -1.847, df = 169, p-value = 0.06649
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.032294757  0.001073984
## sample estimates:
## mean of the differences 
##             -0.01561039

t.test(correct_encoding_to_probe_avg[,4],correct_encoding_to_probe_avg[,3],paired=TRUE)

## 
##  Paired t-test
## 
## data:  correct_encoding_to_probe_avg[, 4] and correct_encoding_to_probe_avg[, 3]
## t = -1.8917, df = 169, p-value = 0.06024
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.0261349659  0.0005568271
## sample estimates:
## mean of the differences 
##             -0.01278907

(encoding_to_probe_plots[["correct_encoding_to_probe_avg"]][["omnibus"]][["high_load_correct"]] + encoding_to_probe_plots[["correct_encoding_to_probe_avg"]][["omnibus"]][["high_load_incorrect"]]) /
  (encoding_to_probe_plots[["correct_encoding_to_probe_avg"]][["omnibus"]][["low_load_correct"]] + 
     encoding_to_probe_plots[["correct_encoding_to_probe_avg"]][["omnibus"]][["low_load_incorrect"]])+
  plot_annotation(title = "Individual trials vs omnibus")

## Warning: Removed 57 rows containing non-finite values (stat_smooth).

## Warning: Removed 57 rows containing missing values (geom_point).

(encoding_to_probe_plots[["correct_encoding_to_probe_avg"]][["L3_Acc"]][["high_load_correct"]] + encoding_to_probe_plots[["correct_encoding_to_probe_avg"]][["L3_Acc"]][["high_load_incorrect"]]) /
  (encoding_to_probe_plots[["correct_encoding_to_probe_avg"]][["L3_Acc"]][["low_load_correct"]] + 
     encoding_to_probe_plots[["correct_encoding_to_probe_avg"]][["L3_Acc"]][["low_load_incorrect"]])+
  plot_annotation(title = "Individual trials vs High Load Accuracy")

## Warning: Removed 57 rows containing non-finite values (stat_smooth).

## Warning: Removed 57 rows containing missing values (geom_point).

(encoding_to_probe_plots[["correct_encoding_to_probe_avg"]][["BPRS"]][["high_load_correct"]] + encoding_to_probe_plots[["correct_encoding_to_probe_avg"]][["BPRS"]][["high_load_incorrect"]]) /
  (encoding_to_probe_plots[["correct_encoding_to_probe_avg"]][["BPRS"]][["low_load_correct"]] + 
     encoding_to_probe_plots[["correct_encoding_to_probe_avg"]][["BPRS"]][["low_load_incorrect"]])+
  plot_annotation(title = "Individual trials vs BPRS Total")

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 58 rows containing non-finite values (stat_smooth).

## Warning: Removed 58 rows containing missing values (geom_point).

Correct template encoding to correct template probe

Nothing significant.

correlations[["correct_encoding_to_correct_probe"]][c(2,3,5),] %>% 
  mutate(
    condition = row.names(.),
    r = cell_spec(r, "html", 
                  color =ifelse(p < 0.05, "black", "grey"))
  )  %>% 
  select(condition,r) %>%
  kable(format = "html", escape = F) %>%
  kable_styling("striped", full_width = F) %>%
  add_header_above(c(" ", "Template Encoding to Template Probe" = 1))

	Template Encoding to Template Probe
condition	r
omnibus_span_no_DFR_MRI	-0.0408674150212371
XDFR_MRI_ACC_L3	-0.00289072384410606
BPRS_TOT.x	0.0402901341367946

encoding_to_probe_plots[["correct_encoding_to_correct_probe"]][["omnibus"]]

encoding_to_probe_plots[["correct_encoding_to_correct_probe"]][["L3_Acc"]]

encoding_to_probe_plots[["correct_encoding_to_correct_probe"]][["BPRS"]]

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

Comparing delay to probe

delay_to_probe_plots <- list()

for (i in c(3,8,9)){
  colnames(similarity_temp[[i]]) <- unlist(similarity_temp[[1]][[1]])
  similarity_temp[[i]][similarity_temp[[i]]==0] <- NA
  temp_plot_data <- cbind.data.frame(data_to_plot,similarity_temp[[i]])
  
  delay_to_probe_plots[[names(similarity_temp)[i]]][["omnibus"]][["low_load_incorrect"]] <- ggplot(data = temp_plot_data,aes(x=omnibus_span_no_DFR_MRI,y=`low load incorrect`))+
    geom_point()+
    stat_smooth(method="lm")+
    ylab("Similarity")+
    ggtitle("Low Load - Incorrect trials")+
    theme_classic()
  
  delay_to_probe_plots[[names(similarity_temp)[i]]][["omnibus"]][["high_load_incorrect"]] <- ggplot(data = temp_plot_data,aes(x=omnibus_span_no_DFR_MRI,y=`high load incorrect`))+
    geom_point()+
    stat_smooth(method="lm")+
    ylab("Similarity")+
    ggtitle("High Load - Incorrect trials")+
    theme_classic()
  
  delay_to_probe_plots[[names(similarity_temp)[i]]][["omnibus"]][["low_load_correct"]] <- ggplot(data = temp_plot_data,aes(x=omnibus_span_no_DFR_MRI,y=`low load correct`))+
    geom_point()+
    stat_smooth(method="lm")+
    ylab("Similarity")+
    ggtitle("Low Load - Correct trials")+
    theme_classic()
  
  delay_to_probe_plots[[names(similarity_temp)[i]]][["omnibus"]][["high_load_correct"]] <- ggplot(data = temp_plot_data,aes(x=omnibus_span_no_DFR_MRI,y=`high load correct`))+
    geom_point()+
    stat_smooth(method="lm")+
    ylab("Similarity")+
    ggtitle("High Load - Correct trials")+
    theme_classic()
  
  delay_to_probe_plots[[names(similarity_temp)[i]]][["L3_Acc"]][["low_load_incorrect"]] <- ggplot(data = temp_plot_data,aes(x=XDFR_MRI_ACC_L3,y=`low load incorrect`))+
    geom_point()+
    stat_smooth(method="lm")+
    ylab("Similarity")+
    ggtitle("Low Load - Incorrect trials")+
    theme_classic()
  
  delay_to_probe_plots[[names(similarity_temp)[i]]][["L3_Acc"]][["high_load_incorrect"]] <- ggplot(data = temp_plot_data,aes(x=XDFR_MRI_ACC_L3,y=`high load incorrect`))+
    geom_point()+
    stat_smooth(method="lm")+
    ylab("Similarity")+
    ggtitle("High Load - Incorrect trials")+
    theme_classic()
  
  delay_to_probe_plots[[names(similarity_temp)[i]]][["L3_Acc"]][["low_load_correct"]] <- ggplot(data = temp_plot_data,aes(x=XDFR_MRI_ACC_L3,y=`low load correct`))+
    geom_point()+
    stat_smooth(method="lm")+
    ylab("Similarity")+
    ggtitle("Low Load - Correct trials")+
    theme_classic()
  
  delay_to_probe_plots[[names(similarity_temp)[i]]][["L3_Acc"]][["high_load_correct"]] <- ggplot(data = temp_plot_data,aes(x=XDFR_MRI_ACC_L3,y=`high load correct`))+
    geom_point()+
    stat_smooth(method="lm")+
    ylab("Similarity")+
    ggtitle("High Load - Correct trials")+
    theme_classic()
  
  delay_to_probe_plots[[names(similarity_temp)[i]]][["BPRS"]][["low_load_incorrect"]] <- ggplot(data = temp_plot_data,aes(x=BPRS_TOT.x,y=`low load incorrect`))+
    geom_point()+
    stat_smooth(method="lm")+
    ylab("Similarity")+
    ggtitle("Low Load - Incorrect trials")+
    theme_classic()
  
  delay_to_probe_plots[[names(similarity_temp)[i]]][["BPRS"]][["high_load_incorrect"]] <- ggplot(data = temp_plot_data,aes(x=BPRS_TOT.x,y=`high load incorrect`))+
    geom_point()+
    stat_smooth(method="lm")+
    ylab("Similarity")+
    ggtitle("High Load - Incorrect trials")+
    theme_classic()
  
  delay_to_probe_plots[[names(similarity_temp)[i]]][["BPRS"]][["low_load_correct"]] <- ggplot(data = temp_plot_data,aes(x=BPRS_TOT.x,y=`low load correct`))+
    geom_point()+
    stat_smooth(method="lm")+
    ylab("Similarity")+
    ggtitle("Low Load - Correct trials")+
    theme_classic()
  
  delay_to_probe_plots[[names(similarity_temp)[i]]][["BPRS"]][["high_load_correct"]] <- ggplot(data = temp_plot_data,aes(x=BPRS_TOT.x,y=`high load correct`))+
    geom_point()+
    stat_smooth(method="lm")+
    ylab("Similarity")+
    ggtitle("High Load - Correct trials")+
    theme_classic()
}

temp_plot_data <- cbind.data.frame(data_to_plot,similarity_temp[["correct_delay_to_correct_probe"]])
colnames(temp_plot_data)[9] <- "correct_delay_probe"

delay_to_probe_plots[["correct_delay_to_correct_probe"]][["omnibus"]] <- ggplot(data = 
                                                                                        temp_plot_data,aes(x=omnibus_span_no_DFR_MRI,y=correct_delay_probe))+
  geom_point()+
  stat_smooth(method="lm")+
  ylab("Similarity")+
  ggtitle("Template delay/probe vs Omnibus Span")+
  theme_classic()

delay_to_probe_plots[["correct_delay_to_correct_probe"]][["BPRS"]] <- ggplot(data = 
                                                                                     temp_plot_data,aes(x=BPRS_TOT.x,y=correct_delay_probe))+
  geom_point()+
  stat_smooth(method="lm")+
  ylab("Similarity")+
  ggtitle("Template delay/probe vs BPRS")+
  theme_classic()

delay_to_probe_plots[["correct_delay_to_correct_probe"]][["L3_Acc"]] <- ggplot(data = 
                                                                                       temp_plot_data,aes(x=XDFR_MRI_ACC_L3,y=correct_delay_probe))+
  geom_point()+
  stat_smooth(method="lm")+
  ylab("Similarity")+
  ggtitle("Template delay/probe vs L3 accuracy")+
  theme_classic()

In these, graphs, if a correlation is black, it is below p < 0.05; if it is not, it is grey.

correlations = list()

for (i in c(3,8,9)){
  colnames(similarity_temp[[i]]) <- unlist(similarity_temp[[1]][[1]])
  temp_list <- list(r = data.frame(matrix(nrow=4,ncol=6)), p = data.frame(matrix(nrow=4,ncol=6)))
  
  for (behav in seq.int(2,7)){
    for (sim in seq.int(1,4)){ 
      temp_corr <- cor.test(similarity_temp[[i]][,sim],data_to_plot[,behav])
      temp_list[["r"]][sim,behav-1] <- temp_corr$estimate
      temp_list[["p"]][sim,behav-1] <- temp_corr$p.value
    }
  }
  
  colnames(temp_list[["r"]]) <- colnames(data_to_plot)[2:7]
  rownames(temp_list[["r"]]) <- colnames(similarity_temp[[i]])
  colnames(temp_list[["p"]]) <- colnames(data_to_plot)[2:7]
  rownames(temp_list[["p"]]) <- colnames(similarity_temp[[i]])
  
  
  correlations[[names(similarity_temp)[i]]] <- temp_list
  
}
temp <- data.frame(r=matrix(nrow=6,ncol=1),p=matrix(nrow=6,ncol=1))
rownames(temp) <- colnames(data_to_plot)[2:7]


for (behav in seq.int(2,7)){
  temp_corr <- cor.test(similarity_temp[["correct_delay_to_correct_probe"]],data_to_plot[,behav])
  temp$r[behav-1] <- temp_corr$estimate
  temp$p[behav-2] <- temp_corr$p.value
}

correlations[["correct_delay_to_correct_probe"]] <- temp

Individual delay to Individual Probe

Here, we see a significant relationship between similarity at low load correct and accuracy.

correlations[["delay_to_probe_avg"]][["r"]] %>% 
  mutate(
    condition = row.names(.),
    omnibus_span_no_DFR_MRI = cell_spec(omnibus_span_no_DFR_MRI, "html", 
                                        color =ifelse(correlations[["delay_to_probe_avg"]][["p"]]$omnibus_span_no_DFR_MRI < 0.05, "black", "grey")),
    XDFR_MRI_ACC_L3 = cell_spec(XDFR_MRI_ACC_L3, "html", 
                                color =ifelse(correlations[["delay_to_probe_avg"]][["p"]]$XDFR_MRI_ACC_L3 < 0.05, "black", "grey")),
    BPRS_TOT.x = cell_spec(BPRS_TOT.x, "html", 
                           color =ifelse(correlations[["delay_to_probe_avg"]][["p"]]$BPRS_TOT.x < 0.05, "black", "grey"))
  ) %>% 
  select(condition,omnibus_span_no_DFR_MRI,XDFR_MRI_ACC_L3,BPRS_TOT.x) %>%
  kable(format = "html", escape = F) %>%
  kable_styling("striped", full_width = F) %>% 
  add_header_above((c(" ", "Individual delay to Individual Probe" = 3)))

	Individual delay to Individual Probe
condition	omnibus_span_no_DFR_MRI	XDFR_MRI_ACC_L3	BPRS_TOT.x
low load incorrect	0.0404533077220652	0.0628571552248336	-0.0386375340860521
high load incorrect	-0.00167161364052409	-0.128985499605605	-0.127055140985302
low load correct	-0.0370425226969129	-0.15554895478408	-0.0307619915617799
high load correct	0.0306317347246807	-0.107039175871987	-0.047238721353328

Can distinguish between load but not accuracy.

delay_to_probe_avg <- fisherz(similarity_temp[["delay_to_probe_avg"]])

t.test(delay_to_probe_avg[,4],delay_to_probe_avg[,2],paired=TRUE)

## 
##  Paired t-test
## 
## data:  delay_to_probe_avg[, 4] and delay_to_probe_avg[, 2]
## t = 1.388, df = 169, p-value = 0.167
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.005455567  0.031294815
## sample estimates:
## mean of the differences 
##              0.01291962

t.test(delay_to_probe_avg[,4],delay_to_probe_avg[,3],paired=TRUE)

## 
##  Paired t-test
## 
## data:  delay_to_probe_avg[, 4] and delay_to_probe_avg[, 3]
## t = 3.0898, df = 169, p-value = 0.002343
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  0.007607663 0.034529418
## sample estimates:
## mean of the differences 
##              0.02106854

(delay_to_probe_plots[["delay_to_probe_avg"]][["omnibus"]][["high_load_correct"]] + delay_to_probe_plots[["delay_to_probe_avg"]][["omnibus"]][["high_load_incorrect"]]) /
  (delay_to_probe_plots[["delay_to_probe_avg"]][["omnibus"]][["low_load_correct"]] + 
     delay_to_probe_plots[["delay_to_probe_avg"]][["omnibus"]][["low_load_incorrect"]])+
  plot_annotation(title = "Individual trials vs omnibus")

## Warning: Removed 57 rows containing non-finite values (stat_smooth).

## Warning: Removed 57 rows containing missing values (geom_point).

(delay_to_probe_plots[["delay_to_probe_avg"]][["L3_Acc"]][["high_load_correct"]] + delay_to_probe_plots[["delay_to_probe_avg"]][["L3_Acc"]][["high_load_incorrect"]]) /
  (delay_to_probe_plots[["delay_to_probe_avg"]][["L3_Acc"]][["low_load_correct"]] + 
     delay_to_probe_plots[["delay_to_probe_avg"]][["L3_Acc"]][["low_load_incorrect"]])+
  plot_annotation(title = "Individual trials vs High Load Accuracy")

## Warning: Removed 57 rows containing non-finite values (stat_smooth).

## Warning: Removed 57 rows containing missing values (geom_point).

(delay_to_probe_plots[["delay_to_probe_avg"]][["BPRS"]][["high_load_correct"]] + delay_to_probe_plots[["delay_to_probe_avg"]][["BPRS"]][["high_load_incorrect"]]) /
  (delay_to_probe_plots[["delay_to_probe_avg"]][["BPRS"]][["low_load_correct"]] + 
     delay_to_probe_plots[["delay_to_probe_avg"]][["BPRS"]][["low_load_incorrect"]])+
  plot_annotation(title = "Individual trials vs BPRS Total")

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 58 rows containing non-finite values (stat_smooth).

## Warning: Removed 58 rows containing missing values (geom_point).

Individual Trial Delay vs Correct Template Probe

No relationships here.

correlations[["delay_to_correct_probe_avg"]][["r"]] %>% 
  mutate(
    condition = row.names(.),
    omnibus_span_no_DFR_MRI = cell_spec(omnibus_span_no_DFR_MRI, "html", 
                                        color =ifelse(correlations[["delay_to_correct_probe_avg"]][["p"]]$omnibus_span_no_DFR_MRI < 0.05, "black", "grey")),
    XDFR_MRI_ACC_L3 = cell_spec(XDFR_MRI_ACC_L3, "html", 
                                color =ifelse(correlations[["delay_to_correct_probe_avg"]][["p"]]$XDFR_MRI_ACC_L3 < 0.05, "black", "grey")),
    BPRS_TOT.x = cell_spec(BPRS_TOT.x, "html", 
                           color =ifelse(correlations[["delay_to_correct_probe_avg"]][["p"]]$BPRS_TOT.x < 0.05, "black", "grey"))
  ) %>% 
  select(condition,omnibus_span_no_DFR_MRI,XDFR_MRI_ACC_L3,BPRS_TOT.x) %>%
  kable(format = "html", escape = F) %>%
  kable_styling("striped", full_width = F) %>% 
  add_header_above((c(" ", "Individual delay to Template probe" = 3)))

	Individual delay to Template probe
condition	omnibus_span_no_DFR_MRI	XDFR_MRI_ACC_L3	BPRS_TOT.x
low load incorrect	-0.00258392767984013	-0.0647831749962899	-0.108872380605308
high load incorrect	-0.00128734837611086	-0.0624134292574505	-0.110652830854019
low load correct	0.112179876532588	-0.0394618061801707	-0.137319901063414
high load correct	0.0226143436002303	-0.0672336450414832	-0.0560933051628179

delay_to_correct_probe_avg <- fisherz(similarity_temp[["delay_to_correct_probe_avg"]])

t.test(delay_to_correct_probe_avg[,4],delay_to_correct_probe_avg[,2],paired=TRUE)

## 
##  Paired t-test
## 
## data:  delay_to_correct_probe_avg[, 4] and delay_to_correct_probe_avg[, 2]
## t = -0.97968, df = 169, p-value = 0.3286
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.025265109  0.008505748
## sample estimates:
## mean of the differences 
##            -0.008379681

t.test(delay_to_correct_probe_avg[,4],delay_to_correct_probe_avg[,3],paired=TRUE)

## 
##  Paired t-test
## 
## data:  delay_to_correct_probe_avg[, 4] and delay_to_correct_probe_avg[, 3]
## t = 0.65507, df = 169, p-value = 0.5133
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.008764226  0.017469311
## sample estimates:
## mean of the differences 
##             0.004352543

(delay_to_probe_plots[["delay_to_correct_probe_avg"]][["omnibus"]][["high_load_correct"]] + delay_to_probe_plots[["delay_to_correct_probe_avg"]][["omnibus"]][["high_load_incorrect"]]) /
  (delay_to_probe_plots[["delay_to_correct_probe_avg"]][["omnibus"]][["low_load_correct"]] + 
     delay_to_probe_plots[["delay_to_correct_probe_avg"]][["omnibus"]][["low_load_incorrect"]])+
  plot_annotation(title = "Individual trials vs omnibus")

## Warning: Removed 57 rows containing non-finite values (stat_smooth).

## Warning: Removed 57 rows containing missing values (geom_point).

(delay_to_probe_plots[["delay_to_correct_probe_avg"]][["L3_Acc"]][["high_load_correct"]] + delay_to_probe_plots[["delay_to_correct_probe_avg"]][["L3_Acc"]][["high_load_incorrect"]]) /
  (delay_to_probe_plots[["delay_to_correct_probe_avg"]][["L3_Acc"]][["low_load_correct"]] + 
     delay_to_probe_plots[["delay_to_correct_probe_avg"]][["L3_Acc"]][["low_load_incorrect"]])+
  plot_annotation(title = "Individual trials vs High Load Accuracy")

## Warning: Removed 57 rows containing non-finite values (stat_smooth).

## Warning: Removed 57 rows containing missing values (geom_point).

(delay_to_probe_plots[["delay_to_correct_probe_avg"]][["BPRS"]][["high_load_correct"]] + delay_to_probe_plots[["delay_to_correct_probe_avg"]][["BPRS"]][["high_load_incorrect"]]) /
  (delay_to_probe_plots[["delay_to_correct_probe_avg"]][["BPRS"]][["low_load_correct"]] + 
     delay_to_probe_plots[["delay_to_correct_probe_avg"]][["BPRS"]][["low_load_incorrect"]])+
  plot_annotation(title = "Individual trials vs BPRS Total")

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 58 rows containing non-finite values (stat_smooth).

## Warning: Removed 58 rows containing missing values (geom_point).

Correct Template delay vs Individual Trial probe

Significant relationship between similarity on high load incorrect trials and span.

correlations[["correct_delay_to_probe_avg"]][["r"]] %>% 
  mutate(
    condition = row.names(.),
    omnibus_span_no_DFR_MRI = cell_spec(omnibus_span_no_DFR_MRI, "html", 
                                        color =ifelse(correlations[["correct_delay_to_probe_avg"]][["p"]]$omnibus_span_no_DFR_MRI < 0.05, "black", "grey")),
    XDFR_MRI_ACC_L3 = cell_spec(XDFR_MRI_ACC_L3, "html", 
                                color =ifelse(correlations[["correct_delay_to_probe_avg"]][["p"]]$XDFR_MRI_ACC_L3 < 0.05, "black", "grey")),
    BPRS_TOT.x = cell_spec(BPRS_TOT.x, "html", 
                           color =ifelse(correlations[["correct_delay_to_probe_avg"]][["p"]]$BPRS_TOT.x < 0.05, "black", "grey"))
  ) %>% 
  select(condition,omnibus_span_no_DFR_MRI,XDFR_MRI_ACC_L3,BPRS_TOT.x) %>%
  kable(format = "html", escape = F) %>%
  kable_styling("striped", full_width = F) %>% 
  add_header_above((c(" ", "Template delay to probe" = 3)))

	Template delay to probe
condition	omnibus_span_no_DFR_MRI	XDFR_MRI_ACC_L3	BPRS_TOT.x
low load incorrect	-0.0126468441837965	0.0545980128216875	-0.0531474614912401
high load incorrect	0.153961519152477	0.0741048231101815	-0.00923587995567198
low load correct	0.0805550991240167	-0.112692711347637	-0.00277214369435476
high load correct	0.127000730668868	0.0684247831820997	-0.034255502613284

Can distinguish between load but not accuracy.

correct_delay_to_probe_avg <- fisherz(similarity_temp[["correct_delay_to_probe_avg"]])

t.test(correct_delay_to_probe_avg[,4],correct_delay_to_probe_avg[,2],paired=TRUE)

## 
##  Paired t-test
## 
## data:  correct_delay_to_probe_avg[, 4] and correct_delay_to_probe_avg[, 2]
## t = -1.3229, df = 169, p-value = 0.1877
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.029060650  0.005739938
## sample estimates:
## mean of the differences 
##             -0.01166036

t.test(correct_delay_to_probe_avg[,4],correct_delay_to_probe_avg[,3],paired=TRUE)

## 
##  Paired t-test
## 
## data:  correct_delay_to_probe_avg[, 4] and correct_delay_to_probe_avg[, 3]
## t = 2.4113, df = 169, p-value = 0.01697
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  0.003593674 0.036043569
## sample estimates:
## mean of the differences 
##              0.01981862

(delay_to_probe_plots[["correct_delay_to_probe_avg"]][["omnibus"]][["high_load_correct"]] + delay_to_probe_plots[["correct_delay_to_probe_avg"]][["omnibus"]][["high_load_incorrect"]]) /
  (delay_to_probe_plots[["correct_delay_to_probe_avg"]][["omnibus"]][["low_load_correct"]] + 
     delay_to_probe_plots[["correct_delay_to_probe_avg"]][["omnibus"]][["low_load_incorrect"]])+
  plot_annotation(title = "Individual trials vs omnibus")

## Warning: Removed 57 rows containing non-finite values (stat_smooth).

## Warning: Removed 57 rows containing missing values (geom_point).

(delay_to_probe_plots[["correct_delay_to_probe_avg"]][["L3_Acc"]][["high_load_correct"]] + delay_to_probe_plots[["correct_delay_to_probe_avg"]][["L3_Acc"]][["high_load_incorrect"]]) /
  (delay_to_probe_plots[["correct_delay_to_probe_avg"]][["L3_Acc"]][["low_load_correct"]] + 
     delay_to_probe_plots[["correct_delay_to_probe_avg"]][["L3_Acc"]][["low_load_incorrect"]])+
  plot_annotation(title = "Individual trials vs High Load Accuracy")

## Warning: Removed 57 rows containing non-finite values (stat_smooth).

## Warning: Removed 57 rows containing missing values (geom_point).

(delay_to_probe_plots[["correct_delay_to_probe_avg"]][["BPRS"]][["high_load_correct"]] + delay_to_probe_plots[["correct_delay_to_probe_avg"]][["BPRS"]][["high_load_incorrect"]]) /
  (delay_to_probe_plots[["correct_delay_to_probe_avg"]][["BPRS"]][["low_load_correct"]] + 
     delay_to_probe_plots[["correct_delay_to_probe_avg"]][["BPRS"]][["low_load_incorrect"]])+
  plot_annotation(title = "Individual trials vs BPRS Total")

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 58 rows containing non-finite values (stat_smooth).

## Warning: Removed 58 rows containing missing values (geom_point).

Correct template delay to correct template probe

Very small correlation between accuracy and similarity, but is significant.

correlations[["correct_delay_to_correct_probe"]][c(2,3,5),] %>% 
  mutate(
    condition = row.names(.),
    r = cell_spec(r, "html", 
                  color =ifelse(p < 0.05, "black", "grey"))
  )  %>% 
  select(condition,r) %>%
  kable(format = "html", escape = F) %>%
  kable_styling("striped", full_width = F) %>%
  add_header_above(c(" ", "Template delay to Template Probe" = 1))

	Template delay to Template Probe
condition	r
omnibus_span_no_DFR_MRI	0.084320931941858
XDFR_MRI_ACC_L3	-0.0730407389792616
BPRS_TOT.x	-0.0540088794276245

delay_to_probe_plots[["correct_delay_to_correct_probe"]][["omnibus"]]

delay_to_probe_plots[["correct_delay_to_correct_probe"]][["L3_Acc"]]

delay_to_probe_plots[["correct_delay_to_correct_probe"]][["BPRS"]]

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).